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Update docs and change integer IDs to hash values
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@ -355,7 +355,7 @@ p
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+row
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+cell #[code ent_id]
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+cell int
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+cell The integer ID of the named entity the token is an instance of.
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+cell The hash value of the named entity the token is an instance of.
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+row
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+cell #[code ent_id_]
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@ -397,13 +397,15 @@ p The L2 norm of the token's vector representation.
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+row
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+cell #[code shape_]
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+cell unicode
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+cell
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| Transform of the tokens's string, to show orthographic features.
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| For example, "Xxxx" or "dd".
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+row
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+cell #[code prefix]
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+cell int
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+cell Integer ID of a length-N substring from the start of the
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+cell
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| Hash value of a length-N substring from the start of the
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| token. Defaults to #[code N=1].
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+row
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@ -417,7 +419,8 @@ p The L2 norm of the token's vector representation.
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+cell #[code suffix]
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+cell int
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+cell
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| Length-N substring from the end of the token. Defaults to #[code N=3].
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| Hash value of a length-N substring from the end of the token.
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| Defaults to #[code N=3].
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+row
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+cell #[code suffix_]
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@ -36,7 +36,7 @@ p Create the vocabulary.
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+cell #[code strings]
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+cell #[code StringStore]
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+cell
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| A #[code StringStore] that maps strings to integers, and vice
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| A #[code StringStore] that maps strings to hash values, and vice
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| versa.
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+footrow
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@ -74,7 +74,7 @@ p
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+row
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+cell #[code id_or_string]
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+cell int / unicode
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+cell The integer ID of a word, or its unicode string.
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+cell The hash value of a word, or its unicode string.
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+footrow
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+cell returns
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@ -12,7 +12,7 @@ p
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p
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| Linguistic annotations are available as
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| #[+api("token#attributes") #[code Token] attributes]. Like many NLP
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| libraries, spaCy #[strong encodes all strings to integers] to reduce
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| libraries, spaCy #[strong encodes all strings to hash values] to reduce
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| memory usage and improve efficiency. So to get the readable string
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| representation of an attribute, we need to add an underscore #[code _]
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| to its name:
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@ -43,7 +43,7 @@ p
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+aside("Why saving the vocab?")
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| Saving the vocabulary with the #[code Doc] is important, because the
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| #[code Vocab] holds the context-independent information about the words,
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| tags and labels, and their #[strong integer IDs]. If the #[code Vocab]
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| tags and labels, and their #[strong hash values]. If the #[code Vocab]
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| wasn't saved with the #[code Doc], spaCy wouldn't know how to resolve
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| those IDs – for example, the word text or the dependency labels. You
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| might be saving #[code 446] for "whale", but in a different vocabulary,
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@ -48,7 +48,7 @@ p
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| #[strong connected by a single arc] in the dependency tree. The term
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| #[strong dep] is used for the arc label, which describes the type of
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| syntactic relation that connects the child to the head. As with other
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| attributes, the value of #[code .dep] is an integer. You can get
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| attributes, the value of #[code .dep] is a hash value. You can get
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| the string value with #[code .dep_].
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+code("Example").
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@ -20,7 +20,7 @@ p
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| The standard way to access entity annotations is the
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| #[+api("doc#ents") #[code doc.ents]] property, which produces a sequence
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| of #[+api("span") #[code Span]] objects. The entity type is accessible
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| either as an integer ID or as a string, using the attributes
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| either as a hash value or as a string, using the attributes
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| #[code ent.label] and #[code ent.label_]. The #[code Span] object acts
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| as a sequence of tokens, so you can iterate over the entity or index into
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| it. You can also get the text form of the whole entity, as though it were
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@ -78,7 +78,7 @@ p
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doc = nlp(u'Netflix is hiring a new VP of global policy')
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# the model didn't recognise any entities :(
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ORG = doc.vocab.strings[u'ORG'] # get integer ID of entity label
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ORG = doc.vocab.strings[u'ORG'] # get hash value of entity label
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netflix_ent = Span(doc, 0, 1, label=ORG) # create a Span for the new entity
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doc.ents = [netflix_ent]
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