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from dataclasses import dataclass
from typing import List, Sequence, Tuple
import datasets
from pie_core import AnnotationLayer, annotation_field
from pie_documents.annotations import LabeledSpan
from pie_documents.documents import TextBasedDocument, TextDocumentWithLabeledSpans
from pie_documents.utils.sequence_tagging import tag_sequence_to_token_spans
from pie_datasets import GeneratorBasedBuilder
def tokens_and_tags_to_text_and_labeled_spans(
tokens: Sequence[str], tags: Sequence[str]
) -> Tuple[str, Sequence[LabeledSpan]]:
start = 0
token_offsets: List[Tuple[int, int]] = []
for token in tokens:
end = start + len(token)
token_offsets.append((start, end))
# we add a space after each token
start = end + 1
text = " ".join(tokens)
spans: List[LabeledSpan] = []
for label, (start, end) in tag_sequence_to_token_spans(tag_sequence=tags):
spans.append(
LabeledSpan(start=token_offsets[start][0], end=token_offsets[end][1], label=label)
)
return text, spans
@dataclass
class CoNLL2003Document(TextBasedDocument):
entities: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
class Conll2003(GeneratorBasedBuilder):
DOCUMENT_TYPE = CoNLL2003Document
BASE_DATASET_PATH = "conll2003"
BASE_DATASET_REVISION = "01ad4ad271976c5258b9ed9b910469a806ff3288"
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="conll2003", version=datasets.Version("1.0.0"), description="CoNLL2003 dataset"
),
]
DOCUMENT_CONVERTERS = {
TextDocumentWithLabeledSpans: {
# just rename the layer
"entities": "labeled_spans",
}
}
def _generate_document_kwargs(self, dataset):
return {"int_to_str": dataset.features["ner_tags"].feature.int2str}
def _generate_document(self, example, int_to_str):
doc_id = example["id"]
tokens = example["tokens"]
ner_tags = [int_to_str(tag) for tag in example["ner_tags"]]
text, ner_spans = tokens_and_tags_to_text_and_labeled_spans(tokens=tokens, tags=ner_tags)
document = CoNLL2003Document(text=text, id=doc_id)
for span in sorted(ner_spans, key=lambda span: span.start):
document.entities.append(span)
return document
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