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Implement Doc.set_ents
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@ -682,6 +682,15 @@ class Errors:
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E1009 = ("String for hash '{val}' not found in StringStore. Set the value "
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"through token.morph_ instead or add the string to the "
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"StringStore with `nlp.vocab.strings.add(string)`.")
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E1010 = ("Unable to set entity information for token {i} which is included "
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"in more than one span in entities, blocked, missing or outside.")
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E1011 = ("Unsupported default '{default}' in doc.set_ents. Available "
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"options: {modes}")
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E1012 = ("Spans provided to doc.set_ents must be provided as a list of "
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"`Span` objects.")
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E1013 = ("Unable to set entity for span with empty label. Entity spans are "
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"required to have a label. To set entity information as missing "
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"or blocked, use the keyword arguments with doc.set_ents.")
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@add_codes
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@ -425,7 +425,7 @@ def test_has_annotation(en_vocab):
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doc[0].lemma_ = "a"
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doc[0].dep_ = "dep"
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doc[0].head = doc[1]
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doc.ents = [Span(doc, 0, 1, label="HELLO"), Span(doc, 1, 2, label="")]
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doc.set_ents([Span(doc, 0, 1, label="HELLO")], default="missing")
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for attr in attrs:
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assert doc.has_annotation(attr)
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@ -455,15 +455,68 @@ def test_is_flags_deprecated(en_tokenizer):
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doc.is_sentenced
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def test_block_ents(en_tokenizer):
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def test_set_ents(en_tokenizer):
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# set ents
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doc = en_tokenizer("a b c d e")
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doc.block_ents([doc[1:2], doc[3:5]])
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doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
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assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 2]
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assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
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# add ents, invalid IOB repaired
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doc = en_tokenizer("a b c d e")
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doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
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doc.set_ents([Span(doc, 0, 2, 12)], default="unmodified")
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assert [t.ent_iob for t in doc] == [3, 1, 3, 2, 2]
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assert [t.ent_type for t in doc] == [12, 12, 11, 0, 0]
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# missing ents
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doc = en_tokenizer("a b c d e")
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doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)], missing=[doc[4:5]])
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assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 0]
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assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
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# outside ents
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doc = en_tokenizer("a b c d e")
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doc.set_ents(
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[Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)],
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outside=[doc[4:5]],
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default="missing",
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)
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assert [t.ent_iob for t in doc] == [3, 3, 1, 0, 2]
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assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
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# blocked ents
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doc = en_tokenizer("a b c d e")
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doc.set_ents([], blocked=[doc[1:2], doc[3:5]], default="unmodified")
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assert [t.ent_iob for t in doc] == [0, 3, 0, 3, 3]
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assert [t.ent_type for t in doc] == [0, 0, 0, 0, 0]
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assert doc.ents == tuple()
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# invalid IOB repaired
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# invalid IOB repaired after blocked
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doc.ents = [Span(doc, 3, 5, "ENT")]
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assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 1]
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doc.block_ents([doc[3:4]])
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doc.set_ents([], blocked=[doc[3:4]], default="unmodified")
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assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 3]
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# all types
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doc = en_tokenizer("a b c d e")
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doc.set_ents(
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[Span(doc, 0, 1, 10)],
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blocked=[doc[1:2]],
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missing=[doc[2:3]],
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outside=[doc[3:4]],
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default="unmodified",
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)
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assert [t.ent_iob for t in doc] == [3, 3, 0, 2, 0]
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assert [t.ent_type for t in doc] == [10, 0, 0, 0, 0]
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doc = en_tokenizer("a b c d e")
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# single span instead of a list
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with pytest.raises(ValueError):
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doc.set_ents([], missing=doc[1:2])
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# invalid default mode
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with pytest.raises(ValueError):
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doc.set_ents([], missing=[doc[1:2]], default="none")
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# conflicting/overlapping specifications
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with pytest.raises(ValueError):
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doc.set_ents([], missing=[doc[1:2]], outside=[doc[1:2]])
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@ -168,7 +168,7 @@ def test_accept_blocked_token():
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ner2 = nlp2.create_pipe("ner", config=config)
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# set "New York" to a blocked entity
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doc2.block_ents([doc2[3:5]])
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doc2.set_ents([], blocked=[doc2[3:5]], default="unmodified")
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assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
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assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
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@ -358,5 +358,5 @@ class BlockerComponent1:
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self.name = name
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def __call__(self, doc):
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doc.block_ents([doc[self.start:self.end]])
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doc.set_ents([], blocked=[doc[self.start:self.end]], default="unmodified")
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return doc
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@ -7,6 +7,7 @@ from libc.stdint cimport int32_t, uint64_t
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import copy
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from collections import Counter
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from enum import Enum
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import numpy
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import srsly
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from thinc.api import get_array_module
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@ -86,6 +87,17 @@ cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name)
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return get_token_attr(token, feat_name)
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class SetEntsDefault(str, Enum):
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blocked = "blocked"
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missing = "missing"
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outside = "outside"
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unmodified = "unmodified"
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@classmethod
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def values(cls):
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return list(cls.__members__.keys())
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cdef class Doc:
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"""A sequence of Token objects. Access sentences and named entities, export
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annotations to numpy arrays, losslessly serialize to compressed binary
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@ -597,9 +609,9 @@ cdef class Doc:
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if i in tokens_in_ents.keys():
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ent_start, ent_end, entity_type, kb_id = tokens_in_ents[i]
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if entity_type is None or entity_type <= 0:
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# Empty label: Missing, unset this token
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ent_iob = 0
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entity_type = 0
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# Only allow labelled spans
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print(i, ent_start, ent_end, entity_type)
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raise ValueError(Errors.E1013)
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elif ent_start == i:
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# Marking the start of an entity
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ent_iob = 3
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@ -611,19 +623,107 @@ cdef class Doc:
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self.c[i].ent_kb_id = kb_id
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self.c[i].ent_iob = ent_iob
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def block_ents(self, spans):
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"""Mark spans as never an entity for the EntityRecognizer.
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def set_ents(self, entities, *, blocked=None, missing=None, outside=None, default=SetEntsDefault.outside):
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"""Set entity annotation.
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spans (List[Span]): The spans to block as never entities.
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entities (List[Span]): Spans with labels to set as entities.
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blocked (Optional[List[Span]]): Spans to set as 'blocked' (never an
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entity) for spacy's built-in NER component. Other components may
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ignore this setting.
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missing (Optional[List[Span]]): Spans with missing/unknown entity
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information.
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outside (Optional[List[Span]]): Spans outside of entities (O in IOB).
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default (str): How to set entity annotation for tokens outside of any
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provided spans. Options: "blocked", "missing", "outside" and
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"unmodified" (preserve current state). Defaults to "outside".
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"""
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for span in spans:
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if default not in SetEntsDefault.values():
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raise ValueError(Errors.E1011.format(default=default, modes=", ".join(SetEntsDefault)))
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if blocked is None:
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blocked = tuple()
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if missing is None:
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missing = tuple()
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if outside is None:
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outside = tuple()
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# Find all tokens covered by spans and check that none are overlapping
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seen_tokens = set()
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for span in entities:
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if not isinstance(span, Span):
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raise ValueError(Errors.E1012.format(span=span))
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for i in range(span.start, span.end):
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if i in seen_tokens:
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raise ValueError(Errors.E1010.format(i=i))
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seen_tokens.add(i)
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for span in blocked:
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if not isinstance(span, Span):
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raise ValueError(Errors.E1012.format(span=span))
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for i in range(span.start, span.end):
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if i in seen_tokens:
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raise ValueError(Errors.E1010.format(i=i))
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seen_tokens.add(i)
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for span in missing:
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if not isinstance(span, Span):
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raise ValueError(Errors.E1012.format(span=span))
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for i in range(span.start, span.end):
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if i in seen_tokens:
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raise ValueError(Errors.E1010.format(i=i))
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seen_tokens.add(i)
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for span in outside:
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if not isinstance(span, Span):
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raise ValueError(Errors.E1012.format(span=span))
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for i in range(span.start, span.end):
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if i in seen_tokens:
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raise ValueError(Errors.E1010.format(i=i))
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seen_tokens.add(i)
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# Set all specified entity information
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for span in entities:
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for i in range(span.start, span.end):
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if not span.label:
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raise ValueError(Errors.E1013)
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if i == span.start:
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self.c[i].ent_iob = 3
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else:
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self.c[i].ent_iob = 1
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self.c[i].ent_type = span.label
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for span in blocked:
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for i in range(span.start, span.end):
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self.c[i].ent_iob = 3
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self.c[i].ent_type = 0
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# if the following token is I, set to B
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if span.end < self.length:
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if self.c[span.end].ent_iob == 1:
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self.c[span.end].ent_iob = 3
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for span in missing:
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for i in range(span.start, span.end):
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self.c[i].ent_iob = 0
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self.c[i].ent_type = 0
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for span in outside:
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for i in range(span.start, span.end):
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self.c[i].ent_iob = 2
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self.c[i].ent_type = 0
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# Set tokens outside of all provided spans
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if default != SetEntsDefault.unmodified:
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for i in range(self.length):
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if i not in seen_tokens:
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self.c[i].ent_type = 0
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if default == SetEntsDefault.outside:
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self.c[i].ent_iob = 2
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elif default == SetEntsDefault.missing:
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self.c[i].ent_iob = 0
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elif default == SetEntsDefault.blocked:
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self.c[i].ent_iob = 3
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# Fix any resulting inconsistent annotation
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for i in range(self.length - 1):
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# I must follow B or I: convert I to B
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if (self.c[i].ent_iob == 0 or self.c[i].ent_iob == 2) and \
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self.c[i+1].ent_iob == 1:
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self.c[i+1].ent_iob = 3
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# Change of type with BI or II: convert second I to B
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if self.c[i].ent_type != self.c[i+1].ent_type and \
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(self.c[i].ent_iob == 3 or self.c[i].ent_iob == 1) and \
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self.c[i+1].ent_iob == 1:
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self.c[i+1].ent_iob = 3
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@property
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def noun_chunks(self):
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@ -288,6 +288,7 @@ def _annot2array(vocab, tok_annot, doc_annot):
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def _add_entities_to_doc(doc, ner_data):
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print(ner_data)
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if ner_data is None:
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return
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elif ner_data == []:
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@ -303,7 +304,14 @@ def _add_entities_to_doc(doc, ner_data):
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spans_from_biluo_tags(doc, ner_data)
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)
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elif isinstance(ner_data[0], Span):
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doc.ents = ner_data
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entities = []
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missing = []
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for span in ner_data:
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if span.label:
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entities.append(span)
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else:
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missing.append(span)
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doc.set_ents(entities, missing=missing)
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else:
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raise ValueError(Errors.E973)
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@ -149,9 +149,10 @@ def spans_from_biluo_tags(doc, tags):
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doc (Doc): The document that the BILUO tags refer to.
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entities (iterable): A sequence of BILUO tags with each tag describing one
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token. Each tags string will be of the form of either "", "O" or
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token. Each tag string will be of the form of either "", "O" or
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"{action}-{label}", where action is one of "B", "I", "L", "U".
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RETURNS (list): A sequence of Span objects.
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RETURNS (list): A sequence of Span objects. Each token with a missing IOB
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tag is returned as a Span with an empty label.
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"""
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token_offsets = tags_to_entities(tags)
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spans = []
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