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Add Doc.lang and Doc.lang_
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@ -272,3 +272,9 @@ def test_doc_is_nered(en_vocab):
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# Test serialization
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
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assert new_doc.is_nered
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def test_doc_lang(en_vocab):
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doc = Doc(en_vocab, words=["Hello", "world"])
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assert doc.lang_ == "en"
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assert doc.lang == en_vocab.strings["en"]
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@ -597,6 +597,16 @@ cdef class Doc:
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if start != self.length:
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yield Span(self, start, self.length)
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@property
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def lang(self):
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"""RETURNS (uint64): ID of the language of the doc's vocabulary."""
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return self.vocab.strings[self.vocab.lang]
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@property
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def lang_(self):
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"""RETURNS (unicode): Language of the doc's vocabulary, e.g. 'en'."""
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return self.vocab.lang
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cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1:
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if self.length == 0:
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# Flip these to false when we see the first token.
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@ -654,6 +654,8 @@ The L2 norm of the document's vector representation.
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| `tensor` <Tag variant="new">2</Tag> | object | Container for dense vector representations. |
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| `cats` <Tag variant="new">2</Tag> | dictionary | Maps either a label to a score for categories applied to whole document, or `(start_char, end_char, label)` to score for categories applied to spans. `start_char` and `end_char` should be character offsets, label can be either a string or an integer ID, and score should be a float. |
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| `user_data` | - | A generic storage area, for user custom data. |
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| `lang` <Tag variant="new">2.1</Tag> | int | Language of the document's vocabulary. |
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| `lang_` <Tag variant="new">2.1</Tag> | unicode | Language of the document's vocabulary. |
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| `is_tagged` | bool | A flag indicating that the document has been part-of-speech tagged. |
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| `is_parsed` | bool | A flag indicating that the document has been syntactically parsed. |
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| `is_sentenced` | bool | A flag indicating that sentence boundaries have been applied to the document. |
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