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@dataclass
class Passage(Document):
"""
Attributes:
passage_id (Optional[str])
"""
passage_id: Optional[str]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.passage_id: Optional[str] = None
@dataclass
class Option(Annotation):
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
@dataclass
class Question(Annotation):
"""
Attributes:
options (FList[Option])
answers (List[int])
"""
options: FList[Option]
answers: List[int]
Automatically generated ontology example_import_ontology. Do not change manually.
"""
from dataclasses import dataclass
from forte.data.data_pack import DataPack
from forte.data.ontology.top import Annotation
from typing import Optional
__all__ = [
"Token",
"EntityMention",
]
@dataclass
class Token(Annotation):
"""
Base parent token entry
Attributes:
pos (Optional[str])
lemma (Optional[str])
"""
pos: Optional[str]
lemma: Optional[str]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.pos: Optional[str] = None
self.lemma: Optional[str] = None
from forte.data.ontology.core import Entry
from forte.data.ontology.core import FList
from forte.data.ontology.top import Annotation
from forte.data.ontology.top import Link
from typing import Optional
__all__ = [
"Token",
"Sentence",
"Document",
"Dependency",
]
@dataclass
class Token(Annotation):
"""
Attributes:
lemma (Optional[str])
is_verb (Optional[bool])
num_chars (Optional[int])
score (Optional[float])
"""
lemma: Optional[str]
is_verb: Optional[bool]
num_chars: Optional[int]
score: Optional[float]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.lemma: Optional[str] = None
def initialize(self, resources: Resources, configs: Config):
if configs.pretrained_model_name in self.name2tokenizer:
self.tokenizer = \
self.name2tokenizer[configs.pretrained_model_name](
pretrained_model_name=configs.pretrained_model_name)
self.encoder = self.name2encoder[configs.pretrained_model_name](
pretrained_model_name=configs.pretrained_model_name)
else:
raise ValueError("Unrecognized pre-trained model name.")
self.entry_type = get_class(configs.entry_type)
if not isinstance(self.entry_type, Annotation) and \
not issubclass(self.entry_type, Annotation):
raise ValueError("entry_type must be annotation type.")
sentiment (Dict[str, float])
"""
speaker: Optional[str]
part_id: Optional[int]
sentiment: Dict[str, float]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.speaker: Optional[str] = None
self.part_id: Optional[int] = None
self.sentiment: Dict[str, float] = dict()
@dataclass
class Phrase(Annotation):
"""
A span based annotation `Phrase`.
Attributes:
phrase_type (Optional[str])
"""
phrase_type: Optional[str]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.phrase_type: Optional[str] = None
@dataclass
class UtteranceContext(Annotation):
"""
entry: EntryType = self.get_entry(entry_id) # type: ignore
yield entry
return
# valid span
if range_annotation is not None:
coverage_index = self.index.coverage_index(type(range_annotation),
entry_type)
if coverage_index is not None:
valid_id &= coverage_index[range_annotation.tid]
range_begin = range_annotation.span.begin if range_annotation else 0
range_end = (range_annotation.span.end if range_annotation else
self.annotations[-1].span.end)
if issubclass(entry_type, Annotation):
temp_begin = Annotation(self, range_begin, range_begin)
begin_index = self.annotations.bisect(temp_begin)
temp_end = Annotation(self, range_end, range_end)
end_index = self.annotations.bisect(temp_end)
# Make sure these temporary annotations are not part of the
# actual data.
temp_begin.regret_creation()
temp_end.regret_creation()
for annotation in self.annotations[begin_index: end_index]:
if annotation.tid not in valid_id:
continue
if (range_annotation is None or
self.index.in_span(annotation, range_annotation.span)):
self.ud_features: Dict[str, str] = dict()
self.ud_misc: Dict[str, str] = dict()
@dataclass
class Document(Annotation):
"""
A span based annotation `Document`, normally used to represent a document.
"""
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
@dataclass
class Sentence(Annotation):
"""
A span based annotation `Sentence`, normally used to represent a sentence.
Attributes:
speaker (Optional[str])
part_id (Optional[int])
sentiment (Dict[str, float])
"""
speaker: Optional[str]
part_id: Optional[int]
sentiment: Dict[str, float]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.speaker: Optional[str] = None
self.part_id: Optional[int] = None
class Utterance(Annotation):
"""
A span based annotation `Utterance`, normally used to represent an utterance in dialogue.
Attributes:
speaker (Optional[str])
"""
speaker: Optional[str]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.speaker: Optional[str] = None
@dataclass
class PredicateArgument(Annotation):
"""
A span based annotation `PredicateArgument`, normally used to represent an argument of a predicate, can be linked to the predicate via the predicate link.
Attributes:
ner_type (Optional[str])
predicate_lemma (Optional[str])
is_verb (Optional[bool])
"""
ner_type: Optional[str]
predicate_lemma: Optional[str]
is_verb: Optional[bool]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.ner_type: Optional[str] = None
self.predicate_lemma: Optional[str] = None
super().__init__(pack, begin, end)
self.phrase_type: Optional[str] = None
@dataclass
class UtteranceContext(Annotation):
"""
`UtteranceContext` represents the context part in dialogue.
"""
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
@dataclass
class Utterance(Annotation):
"""
A span based annotation `Utterance`, normally used to represent an utterance in dialogue.
Attributes:
speaker (Optional[str])
"""
speaker: Optional[str]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.speaker: Optional[str] = None
@dataclass
class PredicateArgument(Annotation):
"""
@dataclass
class Passage(Document):
"""
Attributes:
passage_id (Optional[str])
"""
passage_id: Optional[str]
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
self.passage_id: Optional[str] = None
@dataclass
class Option(Annotation):
def __init__(self, pack: DataPack, begin: int, end: int):
super().__init__(pack, begin, end)
@dataclass
class Question(Annotation):
"""
Attributes:
options (FList[Option])
answers (List[int])
"""
options: FList[Option]
answers: List[int]