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import dataclasses
from typing import Any
import datasets
from pytorch_ie import AnnotationLayer, annotation_field
from pytorch_ie.annotations import BinaryRelation, LabeledSpan, Span
from pytorch_ie.documents import (
TextBasedDocument,
TextDocumentWithLabeledSpansAndBinaryRelations,
)
from pie_datasets import ArrowBasedBuilder, GeneratorBasedBuilder
@dataclasses.dataclass(frozen=True)
class SpanWithIdAndName(Span):
id: str
name: str
def resolve(self) -> Any:
return self.id, self.name, super().resolve()
@dataclasses.dataclass
class TbgaDocument(TextBasedDocument):
entities: AnnotationLayer[SpanWithIdAndName] = annotation_field(target="text")
relations: AnnotationLayer[BinaryRelation] = annotation_field(target="entities")
def example_to_document(example) -> TbgaDocument:
document = TbgaDocument(text=example["text"])
head = SpanWithIdAndName(
# this is due to the original dataset having an integer id but string is required
id=str(example["h"]["id"]),
name=example["h"]["name"],
start=example["h"]["pos"][0],
end=example["h"]["pos"][0] + example["h"]["pos"][1], # end is start + length
)
tail = SpanWithIdAndName(
id=example["t"]["id"],
name=example["t"]["name"],
start=example["t"]["pos"][0],
end=example["t"]["pos"][0] + example["t"]["pos"][1], # end is start + length
)
document.entities.extend([head, tail])
relation = BinaryRelation(head=head, tail=tail, label=example["relation"])
document.relations.append(relation)
return document
def document_to_example(document):
head = document.entities[0]
tail = document.entities[1]
return {
"text": document.text,
"relation": document.relations[0].label,
"h": {"id": int(head.id), "name": head.name, "pos": [head.start, head.end - head.start]},
"t": {"id": tail.id, "name": tail.name, "pos": [tail.start, tail.end - tail.start]},
}
def convert_to_text_document_with_labeled_spans_and_binary_relations(
document: TbgaDocument,
) -> TextDocumentWithLabeledSpansAndBinaryRelations:
text_document = TextDocumentWithLabeledSpansAndBinaryRelations(text=document.text)
old2new_spans = {}
ids = []
names = []
for entity in document.entities: # in our case two entities (head and tail)
# create LabeledSpan and append
labeled_span = LabeledSpan(start=entity.start, end=entity.end, label="ENTITY")
text_document.labeled_spans.append(labeled_span)
# Map the original entity to the new labeled span
old2new_spans[entity] = labeled_span
ids.append(entity.id)
names.append(entity.name)
if len(document.relations) != 1: # one relation between two entities
raise ValueError(f"Expected exactly one relation, got {len(document.relations)}")
old_rel = document.relations[0]
# create BinaryRelation and append
rel = BinaryRelation(
head=old2new_spans[old_rel.head],
tail=old2new_spans[old_rel.tail],
label=old_rel.label,
)
text_document.binary_relations.append(rel)
text_document.metadata["entity_ids"] = ids
text_document.metadata["entity_names"] = names
return text_document
class Tbga(ArrowBasedBuilder):
DOCUMENT_TYPE = TbgaDocument
BASE_DATASET_PATH = "DFKI-SLT/tbga"
BASE_DATASET_REVISION = "78575b79aa1c6ff7712bfa0f0eb0e3d01d80e9bc"
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version("1.0.0"),
description="TBGA dataset",
)
]
DOCUMENT_CONVERTERS = {
TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
}
def _generate_document(self, example, **kwargs):
return example_to_document(example)
def _generate_example(self, document, **kwargs):
return document_to_example(document)
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