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Update examples and API docs
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ines
parent
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commit
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@ -109,9 +109,8 @@ p
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| easily accessed.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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for token in doc:
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print(token.text, token.tag_)
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doc = nlp(u'Give it back')
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assert [t.text for t in doc] == [u'Give', u'it', u'back']
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p
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| This is the main way of accessing #[+api("token") #[code Token]] objects,
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@ -143,7 +142,7 @@ p Get the number of tokens in the document.
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+h(2, "similarity") Doc.similarity
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+tag method
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+tag requires model
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+tag requires model: vectors
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p
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| Make a semantic similarity estimate. The default estimate is cosine
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@ -178,11 +177,10 @@ p
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| of the given attribute ID.
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+aside-code("Example").
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from spacy import attrs
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from spacy.attrs import ORTH
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doc = nlp(u'apple apple orange banana')
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tokens.count_by(attrs.ORTH)
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# {12800L: 1, 11880L: 2, 7561L: 1}
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tokens.to_array([attrs.ORTH])
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assert doc.count_by(ORTH) == {7024L: 1, 119552L: 1, 2087L: 2}
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doc.to_array([attrs.ORTH])
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# array([[11880], [11880], [7561], [12800]])
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+table(["Name", "Type", "Description"])
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@ -237,6 +235,7 @@ p
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np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
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doc2 = Doc(doc.vocab)
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doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array)
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assert doc.text == doc2.text
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+table(["Name", "Type", "Description"])
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+row
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@ -307,8 +306,7 @@ p
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+aside-code("Example").
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doc = nlp(u'Los Angeles start.')
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doc.merge(0, len('Los Angeles'), 'NNP', 'Los Angeles', 'GPE')
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print([token.text for token in doc])
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# ['Los Angeles', 'start', '.']
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assert [t.text for t in doc] == [u'Los Angeles', u'start', u'.']
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+table(["Name", "Type", "Description"])
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+row
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@ -338,7 +336,7 @@ p
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+h(2, "print_tree") Doc.print_tree
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+tag method
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+tag requires model
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+tag requires model: parse
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p
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| Returns the parse trees in JSON (dict) format. Especially useful for
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@ -371,7 +369,7 @@ p
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+h(2, "ents") Doc.ents
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+tag property
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+tag requires model
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+tag requires model: NER
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p
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| Iterate over the entities in the document. Yields named-entity
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@ -393,7 +391,7 @@ p
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+h(2, "noun_chunks") Doc.noun_chunks
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+tag property
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+tag requires model
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+tag requires model: parse
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p
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| Iterate over the base noun phrases in the document. Yields base
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@ -416,7 +414,7 @@ p
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+h(2, "sents") Doc.sents
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+tag property
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+tag requires model
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+tag requires model: parse
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p
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| Iterate over the sentences in the document. Sentence spans have no label.
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@ -438,7 +436,7 @@ p
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+h(2, "has_vector") Doc.has_vector
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+tag property
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+tag requires model
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+tag requires 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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@ -456,7 +454,7 @@ p
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+h(2, "vector") Doc.vector
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+tag property
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+tag requires model
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+tag requires model: vectors
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p
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| A real-valued meaning representation. Defaults to an average of the
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@ -464,8 +462,8 @@ p
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+aside-code("Example").
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apples = nlp(u'I like apples')
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(apples.vector.dtype, apples.vector.shape)
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# (dtype('float32'), (300,))
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assert doc.vector.dtype == 'float32'
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assert doc.vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+footrow
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@ -475,11 +473,18 @@ p
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+h(2, "vector_norm") Doc.vector_norm
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+tag property
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+tag requires model
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+tag requires model: vectors
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p
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| The L2 norm of the document's vector representation.
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+aside-code("Example").
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doc1 = nlp(u'I like apples')
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doc2 = nlp(u'I like oranges')
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doc1.vector_norm # 4.54232424414368
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doc2.vector_norm # 3.304373298575751
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assert doc1.vector_norm != doc2.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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@ -12,8 +12,7 @@ p Create a Span object from the #[code slice doc[start : end]].
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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print([token.text for token in span])
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# ['it', 'back', '!']
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assert [t.text for t in span] == [u'it', u'back', u'!']
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+table(["Name", "Type", "Description"])
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+row
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@ -93,8 +92,7 @@ p Iterate over #[code Token] objects.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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print([token.text for token in span])
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# ['it', 'back', '!']
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assert [t.text for t in span] == ['it', 'back', '!']
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+table(["Name", "Type", "Description"])
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+footrow
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@ -120,16 +118,18 @@ p Get the number of tokens in the span.
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+h(2, "similarity") Span.similarity
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+tag method
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+tag requires model
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+tag requires model: vectors
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p
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| Make a semantic similarity estimate. The default estimate is cosine
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| similarity using an average of word vectors.
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+aside-code("Example").
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apples, and, oranges = nlp(u'apples and oranges')
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apples_oranges = apples.similarity(oranges)
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oranges_apples = oranges.similarity(apples)
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doc = nlp(u'green apples and red oranges')
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green_apples = doc[:2]
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red_oranges = doc[3:]
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apples_oranges = green_apples.similarity(red_oranges)
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oranges_apples = red_oranges.similarity(green_apples)
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assert apples_oranges == oranges_apples
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+table(["Name", "Type", "Description"])
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@ -165,17 +165,18 @@ p Retokenize the document, such that the span is merged into a single token.
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+h(2, "root") Span.root
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+tag property
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+tag requires model: parse
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p
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| The token within the span that's highest in the parse tree. If there's a
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| tie, the earlist is prefered.
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+aside-code("Example").
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tokens = nlp(u'I like New York in Autumn.')
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i, like, new, york, in_, autumn, dot = range(len(tokens))
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assert tokens[new].head.text == 'York'
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assert tokens[york].head.text == 'like'
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new_york = tokens[new:york+1]
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doc = nlp(u'I like New York in Autumn.')
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i, like, new, york, in_, autumn, dot = range(len(doc))
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assert doc[new].head.text == 'York'
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assert doc[york].head.text == 'like'
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new_york = doc[new:york+1]
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assert new_york.root.text == 'York'
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+table(["Name", "Type", "Description"])
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@ -186,9 +187,15 @@ p
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+h(2, "lefts") Span.lefts
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+tag property
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+tag requires model: parse
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p Tokens that are to the left of the span, whose head is within the span.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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lefts = [t.text for t in doc[3:7].lefts]
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assert lefts == [u'New']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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@ -197,9 +204,15 @@ p Tokens that are to the left of the span, whose head is within the span.
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+h(2, "rights") Span.rights
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+tag property
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+tag requires model: parse
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p Tokens that are to the right of the span, whose head is within the span.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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rights = [t.text for t in doc[2:4].rights]
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assert rights == [u'in']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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@ -208,9 +221,15 @@ p Tokens that are to the right of the span, whose head is within the span.
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+h(2, "subtree") Span.subtree
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+tag property
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+tag requires model: parse
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p Tokens that descend from tokens in the span, but fall outside it.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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subtree = [t.text for t in doc[:3].subtree]
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assert subtree == [u'Give', u'it', u'back', u'!']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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@ -219,15 +238,15 @@ p Tokens that descend from tokens in the span, but fall outside it.
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+h(2, "has_vector") Span.has_vector
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+tag property
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+tag requires model
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+tag requires 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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| object.
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+aside-code("Example").
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apple = nlp(u'apple')
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assert apple.has_vector
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doc = nlp(u'I like apples')
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assert doc[1:].has_vector
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+table(["Name", "Type", "Description"])
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+footrow
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@ -237,16 +256,16 @@ p
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+h(2, "vector") Span.vector
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+tag property
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+tag requires model
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+tag requires model: vectors
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p
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| A real-valued meaning representation. Defaults to an average of the
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| token vectors.
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+aside-code("Example").
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apple = nlp(u'apple')
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(apple.vector.dtype, apple.vector.shape)
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# (dtype('float32'), (300,))
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doc = nlp(u'I like apples')
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assert doc[1:].vector.dtype == 'float32'
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assert doc[1:].vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+footrow
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@ -256,11 +275,17 @@ p
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+h(2, "vector_norm") Span.vector_norm
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+tag property
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+tag requires model
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+tag requires model: vectors
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p
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| The L2 norm of the span's vector representation.
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+aside-code("Example").
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doc = nlp(u'I like apples')
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doc[1:].vector_norm # 4.800883928527915
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doc[2:].vector_norm # 6.895897646384268
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assert doc[1:].vector_norm != doc[2:].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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@ -12,6 +12,7 @@ p Construct a #[code Token] object.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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token = doc[0]
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assert token.text == u'Give'
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+table(["Name", "Type", "Description"])
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+row
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@ -59,8 +60,7 @@ p Check the value of a boolean flag.
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from spacy.attrs import IS_TITLE
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doc = nlp(u'Give it back! He pleaded.')
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token = doc[0]
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token.check_flag(IS_TITLE)
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# True
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assert token.check_flag(IS_TITLE) == True
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+table(["Name", "Type", "Description"])
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+row
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@ -73,35 +73,14 @@ p Check the value of a boolean flag.
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+cell bool
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+cell Whether the flag is set.
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+h(2, "nbor") Token.nbor
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+tag method
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p Get a neighboring token.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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token = doc[0]
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token.nbor()
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# it
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code i]
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+cell int
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+cell The relative position of the token to get. Defaults to #[code 1].
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+footrow
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+cell returns
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+cell #[code Token]
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+cell The token at position #[code self.doc[self.i+i]].
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+h(2, "similarity") Token.similarity
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+tag method
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+tag requires 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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apples, and, oranges = nlp(u'apples and oranges')
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apples, _, oranges = nlp(u'apples and oranges')
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apples_oranges = apples.similarity(oranges)
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oranges_apples = oranges.similarity(apples)
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assert apples_oranges == oranges_apples
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@ -119,13 +98,41 @@ p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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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, "nbor") Token.nbor
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+tag method
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p Get a neighboring token.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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give_nbor = doc[0].nbor()
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assert give_nbor.text == u'it'
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code i]
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+cell int
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+cell The relative position of the token to get. Defaults to #[code 1].
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+footrow
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+cell returns
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+cell #[code Token]
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+cell The token at position #[code self.doc[self.i+i]].
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+h(2, "is_ancestor") Token.is_ancestor
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+tag method
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+tag requires model: parse
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p
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| Check whether this token is a parent, grandparent, etc. of another
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| in the dependency tree.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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give = doc[0]
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it = doc[1]
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assert give.is_ancestor(it)
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+table(["Name", "Type", "Description"])
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+row
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+cell descendant
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@ -137,60 +144,38 @@ p
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+cell bool
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+cell Whether this token is the ancestor of the descendant.
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+h(2, "has_vector") Token.has_vector
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+h(2, "ancestors") Token.ancestors
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+tag property
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+tag requires model
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+tag requires model: parse
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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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| token.
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p The rightmost token of this token's syntactic descendants.
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+aside-code("Example").
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apple = nlp(u'apple')
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assert apple.has_vector
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doc = nlp(u'Give it back! He pleaded.')
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it_ancestors = doc[1].ancestors
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assert [t.text for t in it_ancestors] == [u'Give']
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he_ancestors = doc[4].ancestors
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assert [t.text for t in he_ancestors] == [u'pleaded']
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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 token has a vector data attached.
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+h(2, "vector") Token.vector
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+tag property
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+tag requires model
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p
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| A real-valued meaning representation.
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+aside-code("Example").
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apple = nlp(u'apple')
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(apple.vector.dtype, apple.vector.shape)
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# (dtype('float32'), (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 token's semantics.
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+h(2, "vector_norm") Span.vector_norm
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+tag property
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+tag requires model
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p
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| The L2 norm of the token's vector 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 float
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+cell The L2 norm of the vector representation.
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+cell yields
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+cell #[code Token]
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+cell
|
||||
| A sequence of ancestor tokens such that
|
||||
| #[code ancestor.is_ancestor(self)].
|
||||
|
||||
+h(2, "conjuncts") Token.conjuncts
|
||||
+tag property
|
||||
+tag requires model: parse
|
||||
|
||||
p A sequence of coordinated tokens, including the token itself.
|
||||
|
||||
+aside-code("Example").
|
||||
doc = nlp(u'I like apples and oranges')
|
||||
apples_conjuncts = doc[2].conjuncts
|
||||
assert [t.text for t in apples_conjuncts] == [u'oranges']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+cell yields
|
||||
|
|
@ -199,9 +184,15 @@ p A sequence of coordinated tokens, including the token itself.
|
|||
|
||||
+h(2, "children") Token.children
|
||||
+tag property
|
||||
+tag requires model: parse
|
||||
|
||||
p A sequence of the token's immediate syntactic children.
|
||||
|
||||
+aside-code("Example").
|
||||
doc = nlp(u'Give it back! He pleaded.')
|
||||
give_children = doc[0].children
|
||||
assert [t.text for t in give_children] == [u'it', u'back', u'!']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+cell yields
|
||||
|
|
@ -210,27 +201,79 @@ p A sequence of the token's immediate syntactic children.
|
|||
|
||||
+h(2, "subtree") Token.subtree
|
||||
+tag property
|
||||
+tag requires model: parse
|
||||
|
||||
p A sequence of all the token's syntactic descendents.
|
||||
|
||||
+aside-code("Example").
|
||||
doc = nlp(u'Give it back! He pleaded.')
|
||||
give_subtree = doc[0].subtree
|
||||
assert [t.text for t in give_subtree] == [u'Give', u'it', u'back', u'!']
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell A descendant token such that #[code self.is_ancestor(descendant)].
|
||||
|
||||
+h(2, "ancestors") Token.ancestors
|
||||
+h(2, "has_vector") Token.has_vector
|
||||
+tag property
|
||||
+tag requires model: vectors
|
||||
|
||||
p The rightmost token of this token's syntactic descendants.
|
||||
p
|
||||
| A boolean value indicating whether a word vector is associated with the
|
||||
| token.
|
||||
|
||||
+aside-code("Example").
|
||||
doc = nlp(u'I like apples')
|
||||
apples = doc[2]
|
||||
assert apples.has_vector
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+cell yields
|
||||
+cell #[code Token]
|
||||
+cell
|
||||
| A sequence of ancestor tokens such that
|
||||
| #[code ancestor.is_ancestor(self)].
|
||||
+cell returns
|
||||
+cell bool
|
||||
+cell Whether the token has a vector data attached.
|
||||
|
||||
+h(2, "vector") Token.vector
|
||||
+tag property
|
||||
+tag requires model: vectors
|
||||
|
||||
p
|
||||
| A real-valued meaning representation.
|
||||
|
||||
+aside-code("Example").
|
||||
doc = nlp(u'I like apples')
|
||||
apples = doc[2]
|
||||
assert apples.vector.dtype == 'float32'
|
||||
assert apples.vector.shape == (300,)
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+cell returns
|
||||
+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
|
||||
+cell A 1D numpy array representing the token's semantics.
|
||||
|
||||
+h(2, "vector_norm") Span.vector_norm
|
||||
+tag property
|
||||
+tag requires model: vectors
|
||||
|
||||
p
|
||||
| The L2 norm of the token's vector representation.
|
||||
|
||||
+aside-code("Example").
|
||||
doc = nlp(u'I like apples and pasta')
|
||||
apples = doc[2]
|
||||
pasta = doc[4]
|
||||
apples.vector_norm # 6.89589786529541
|
||||
pasta.vector_norm # 7.759851932525635
|
||||
assert apples.vector_norm != pasta.vector_norm
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+footrow
|
||||
+cell returns
|
||||
+cell float
|
||||
+cell The L2 norm of the vector representation.
|
||||
|
||||
+h(2, "attributes") Attributes
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user