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Update docstrings and API docs for Lexeme
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ines
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@ -30,19 +30,16 @@ memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
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cdef class Lexeme:
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"""
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An entry in the vocabulary. A Lexeme has no string context --- it's a
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"""An entry in the vocabulary. A `Lexeme` has no string context – it's a
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word-type, as opposed to a word token. It therefore has no part-of-speech
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tag, dependency parse, or lemma (lemmatization depends on the part-of-speech
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tag).
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"""
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def __init__(self, Vocab vocab, int orth):
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"""
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Create a Lexeme object.
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"""Create a Lexeme object.
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Arguments:
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vocab (Vocab): The parent vocabulary
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orth (int): The orth id of the lexeme.
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vocab (Vocab): The parent vocabulary
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orth (int): The orth id of the lexeme.
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Returns (Lexeme): The newly constructd object.
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"""
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self.vocab = vocab
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@ -82,35 +79,28 @@ cdef class Lexeme:
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return self.c.orth
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def set_flag(self, attr_id_t flag_id, bint value):
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"""
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Change the value of a boolean flag.
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"""Change the value of a boolean flag.
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Arguments:
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flag_id (int): The attribute ID of the flag to set.
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value (bool): The new value of the flag.
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flag_id (int): The attribute ID of the flag to set.
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value (bool): The new value of the flag.
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"""
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Lexeme.c_set_flag(self.c, flag_id, value)
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def check_flag(self, attr_id_t flag_id):
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"""
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Check the value of a boolean flag.
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"""Check the value of a boolean flag.
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Arguments:
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flag_id (int): The attribute ID of the flag to query.
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Returns (bool): The value of the flag.
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flag_id (int): The attribute ID of the flag to query.
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RETURNS (bool): The value of the flag.
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"""
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return True if Lexeme.c_check_flag(self.c, flag_id) else False
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def similarity(self, other):
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"""
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Compute a semantic similarity estimate. Defaults to cosine over vectors.
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"""Compute a semantic similarity estimate. Defaults to cosine over
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vectors.
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Arguments:
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other:
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The object to compare with. By default, accepts Doc, Span,
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Token and Lexeme objects.
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Returns:
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score (float): A scalar similarity score. Higher is more similar.
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other (object): The object to compare with. By default, accepts `Doc`,
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`Span`, `Token` and `Lexeme` objects.
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RETURNS (float): A scalar similarity score. Higher is more similar.
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"""
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if self.vector_norm == 0 or other.vector_norm == 0:
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return 0.0
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@ -140,6 +130,11 @@ cdef class Lexeme:
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self.orth = self.c.orth
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property has_vector:
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"""A boolean value indicating whether a word vector is associated with
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the object.
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RETURNS (bool): Whether a word vector is associated with the object.
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"""
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def __get__(self):
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cdef int i
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for i in range(self.vocab.vectors_length):
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@ -149,6 +144,10 @@ cdef class Lexeme:
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return False
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property vector_norm:
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"""The L2 norm of the lexeme's vector representation.
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RETURNS (float): The L2 norm of the vector representation.
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"""
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def __get__(self):
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return self.c.l2_norm
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@ -156,6 +155,11 @@ cdef class Lexeme:
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self.c.l2_norm = value
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property vector:
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"""A real-valued meaning representation.
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RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
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representing the lexeme's semantics.
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"""
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def __get__(self):
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cdef int length = self.vocab.vectors_length
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if length == 0:
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@ -196,6 +200,14 @@ cdef class Lexeme:
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def __get__(self):
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return self.vocab.strings[self.c.orth]
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property text:
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"""A unicode representation of the token text.
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RETURNS (unicode): The original verbatim text of the token.
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"""
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def __get__(self):
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return self.orth_
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property lower:
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def __get__(self): return self.c.lower
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def __set__(self, int x): self.c.lower = x
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@ -2,7 +2,154 @@
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include ../../_includes/_mixins
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p An entry in the vocabulary.
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p
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| An entry in the vocabulary. A #[code Lexeme] has no string context – it's
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| a word-type, as opposed to a word token. It therefore has no
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| part-of-speech tag, dependency parse, or lemma (if lemmatization depends
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| on the part-of-speech tag).
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+h(2, "init") Lexeme.__init__
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+tag method
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p Create a #[code Lexeme] object.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The parent vocabulary.
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+row
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+cell #[code orth]
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+cell int
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+cell The orth id of the lexeme.
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+footrow
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+cell returns
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+cell #[code Lexeme]
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+cell The newly constructed object.
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+h(2, "set_flag") Lexeme.set_flag
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+tag method
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p Change the value of a boolean flag.
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+aside-code("Example").
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COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
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nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to set.
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+row
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+cell #[code value]
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+cell bool
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+cell The new value of the flag.
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+h(2, "check_flag") Lexeme.check_flag
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+tag method
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p Check the value of a boolean flag.
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+aside-code("Example").
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is_my_library = lambda text: text in ['spaCy', 'Thinc']
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MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
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assert nlp.vocab[u'spaCy'].check_flag(MY_LIBRARY) == True
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to query.
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+footrow
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+cell returns
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+cell bool
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+cell The value of the flag.
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+h(2, "similarity") Lexeme.similarity
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+tag method
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+tag-model("vectors")
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p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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orange = nlp.vocab[u'orange']
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apple_orange = apple.similarity(orange)
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orange_apple = orange.similarity(apple)
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assert apple_orange == orange_apple
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+table(["Name", "Type", "Description"])
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+row
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+cell other
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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+footrow
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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+h(2, "has_vector") Lexeme.has_vector
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+tag property
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+tag-model("vectors")
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p
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| A boolean value indicating whether a word vector is associated with the
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| lexeme.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.has_vector
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell bool
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+cell Whether the lexeme has a vector data attached.
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+h(2, "vector") Lexeme.vector
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+tag property
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+tag-model("vectors")
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p A real-valued meaning representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.vector.dtype == 'float32'
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assert apple.vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the lexeme's semantics.
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+h(2, "vector_norm") Lexeme.vector_norm
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+tag property
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+tag-model("vectors")
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p The L2 norm of the lexeme's vector representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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pasta = nlp.vocab[u'pasta']
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apple.vector_norm # 7.1346845626831055
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pasta.vector_norm # 7.759851932525635
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assert apple.vector_norm != pasta.vector_norm
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell float
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+cell The L2 norm of the vector representation.
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+h(2, "attributes") Attributes
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@ -12,6 +159,16 @@ p An entry in the vocabulary.
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+cell #[code Vocab]
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+cell
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+row
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+cell #[code text]
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+cell unicode
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+cell Verbatim text content.
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+row
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+cell #[code lex_id]
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+cell int
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+cell ID of the lexeme's lexical type.
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+row
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+cell #[code lower]
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+cell int
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@ -124,116 +281,9 @@ p An entry in the vocabulary.
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+row
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+cell #[code prob]
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+cell float
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+cell Smoothed log probability estimate of token's type.
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+cell Smoothed log probability estimate of lexeme's type.
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+row
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+cell #[code sentiment]
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+cell float
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+cell A scalar value indicating the positivity or negativity of the token.
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+row
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+cell #[code lex_id]
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+cell int
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+cell ID of the token's lexical type.
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+row
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+cell #[code text]
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+cell unicode
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+cell Verbatim text content.
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+h(2, "init") Lexeme.__init__
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+tag method
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p Create a #[code Lexeme] object.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The parent vocabulary.
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+row
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+cell #[code orth]
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+cell int
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+cell The orth id of the lexeme.
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+footrow
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+cell returns
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+cell #[code Lexeme]
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+cell The newly constructed object.
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+h(2, "set_flag") Lexeme.set_flag
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+tag method
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p Change the value of a boolean flag.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to set.
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+row
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+cell #[code value]
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+cell bool
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+cell The new value of the flag.
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+footrow
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+cell returns
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+cell #[code None]
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+cell -
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+h(2, "check_flag") Lexeme.check_flag
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+tag method
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p Check the value of a boolean flag.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to query.
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+footrow
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+cell returns
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+cell bool
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+cell The value of the flag.
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+h(2, "similarity") Lexeme.similarity
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+tag method
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p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code other]
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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+footrow
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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+h(2, "vector") Lexeme.vector
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+tag property
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p A real-valued meaning representation.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell A real-valued meaning representation.
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+h(2, "has_vector") Lexeme.has_vector
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+tag property
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p A boolean value indicating whether a word vector is associated with the object.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell bool
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+cell Whether a word vector is associated with the object.
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+cell A scalar value indicating the positivity or negativity of the lexeme.
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