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import dataclasses
import logging
from typing import Any, Dict, List, Tuple
import datasets
from pie_modules.document.processing import token_based_document_to_text_based
from pytorch_ie.annotations import BinaryRelation, LabeledSpan
from pytorch_ie.core import AnnotationList, annotation_field
from pytorch_ie.documents import (
TextDocumentWithLabeledSpansAndBinaryRelations,
TokenBasedDocument,
)
from pytorch_ie.utils.span import bio_tags_to_spans
from pie_datasets import GeneratorBasedBuilder
log = logging.getLogger(__name__)
def labels_and_spans_to_bio_tags(
labels: List[str], spans: List[Tuple[int, int]], sequence_length: int
) -> List[str]:
bio_tags = ["O"] * sequence_length
for label, (start, end) in zip(labels, spans):
bio_tags[start] = f"B-{label}"
for i in range(start + 1, end):
bio_tags[i] = f"I-{label}"
return bio_tags
@dataclasses.dataclass
class SciDTBArgminDocument(TokenBasedDocument):
units: AnnotationList[LabeledSpan] = annotation_field(target="tokens")
relations: AnnotationList[BinaryRelation] = annotation_field(target="units")
@dataclasses.dataclass
class SimplifiedSciDTBArgminDocument(TokenBasedDocument):
labeled_spans: AnnotationList[LabeledSpan] = annotation_field(target="tokens")
binary_relations: AnnotationList[BinaryRelation] = annotation_field(target="labeled_spans")
def example_to_document(
example: Dict[str, Any],
unit_bio: datasets.ClassLabel,
unit_label: datasets.ClassLabel,
relation: datasets.ClassLabel,
):
document = SciDTBArgminDocument(id=example["id"], tokens=tuple(example["data"]["token"]))
bio_tags = unit_bio.int2str(example["data"]["unit-bio"])
unit_labels = unit_label.int2str(example["data"]["unit-label"])
roles = relation.int2str(example["data"]["role"])
tag_sequence = [
f"{bio}-{label}|{role}|{parent_offset}"
for bio, label, role, parent_offset in zip(
bio_tags, unit_labels, roles, example["data"]["parent-offset"]
)
]
spans_with_label = sorted(
bio_tags_to_spans(tag_sequence), key=lambda label_and_span: label_and_span[1][0]
)
labels, spans = zip(*spans_with_label)
span_unit_labels, span_roles, span_parent_offsets = zip(
*[label.split("|") for label in labels]
)
units = [
LabeledSpan(start=start, end=end + 1, label=label)
for (start, end), label in zip(spans, span_unit_labels)
]
document.units.extend(units)
# The relation direction is as in "f{head} {relation_label} {tail}"
relations = []
for idx, parent_offset in enumerate(span_parent_offsets):
if span_roles[idx] != "none":
relations.append(
BinaryRelation(
head=units[idx], tail=units[idx + int(parent_offset)], label=span_roles[idx]
)
)
document.relations.extend(relations)
return document
def document_to_example(
document: SciDTBArgminDocument,
unit_bio: datasets.ClassLabel,
unit_label: datasets.ClassLabel,
relation: datasets.ClassLabel,
) -> Dict[str, Any]:
unit2idx = {unit: idx for idx, unit in enumerate(document.units)}
unit2parent_relation = {relation.head: relation for relation in document.relations}
unit_labels = [unit.label for unit in document.units]
roles = [
unit2parent_relation[unit].label if unit in unit2parent_relation else "none"
for unit in document.units
]
parent_offsets = [
unit2idx[unit2parent_relation[unit].tail] - idx if unit in unit2parent_relation else 0
for idx, unit in enumerate(document.units)
]
labels = [
f"{unit_label}-{role}-{parent_offset}"
for unit_label, role, parent_offset in zip(unit_labels, roles, parent_offsets)
]
tag_sequence = labels_and_spans_to_bio_tags(
labels=labels,
spans=[(unit.start, unit.end) for unit in document.units],
sequence_length=len(document.tokens),
)
bio_tags, unit_labels, roles, parent_offsets = zip(
*[tag.split("-", maxsplit=3) for tag in tag_sequence]
)
data = {
"token": list(document.tokens),
"unit-bio": unit_bio.str2int(bio_tags),
"unit-label": unit_label.str2int(unit_labels),
"role": relation.str2int(roles),
"parent-offset": [int(idx_str) for idx_str in parent_offsets],
}
result = {"id": document.id, "data": data}
return result
def convert_to_text_document_with_labeled_spans_and_binary_relations(
document: SciDTBArgminDocument,
) -> TextDocumentWithLabeledSpansAndBinaryRelations:
doc_simplified = document.as_type(
SimplifiedSciDTBArgminDocument,
field_mapping={"units": "labeled_spans", "relations": "binary_relations"},
)
result = token_based_document_to_text_based(
doc_simplified,
result_document_type=TextDocumentWithLabeledSpansAndBinaryRelations,
join_tokens_with=" ",
)
return result
class SciDTBArgmin(GeneratorBasedBuilder):
DOCUMENT_TYPE = SciDTBArgminDocument
DOCUMENT_CONVERTERS = {
TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
}
BASE_DATASET_PATH = "DFKI-SLT/scidtb_argmin"
BASE_DATASET_REVISION = "8c02587edcb47ab5b102692bd10bfffd1844a09b"
BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")]
DEFAULT_CONFIG_NAME = "default"
def _generate_document_kwargs(self, dataset):
return {
"unit_bio": dataset.features["data"].feature["unit-bio"],
"unit_label": dataset.features["data"].feature["unit-label"],
"relation": dataset.features["data"].feature["role"],
}
def _generate_document(self, example, unit_bio, unit_label, relation):
return example_to_document(
example,
unit_bio=unit_bio,
unit_label=unit_label,
relation=relation,
)
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