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import dataclasses |
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from typing import Any, Dict, List |
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import datasets |
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from pytorch_ie.core import ( |
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Annotation, |
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AnnotationLayer, |
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AnnotationList, |
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annotation_field, |
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) |
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from pytorch_ie.documents import TextBasedDocument |
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from pie_datasets import GeneratorBasedBuilder |
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@dataclasses.dataclass(eq=True, frozen=True) |
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class AbstractiveSummary(Annotation): |
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"""A question about a context.""" |
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text: str |
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def __str__(self) -> str: |
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return self.text |
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@dataclasses.dataclass(eq=True, frozen=True) |
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class SectionName(Annotation): |
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"""A question about a context.""" |
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text: str |
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def __str__(self) -> str: |
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return self.text |
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@dataclasses.dataclass |
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class ScientificPapersDocument(TextBasedDocument): |
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"""A PIE document for scientific papers dataset.""" |
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abstract: AnnotationLayer[AbstractiveSummary] = annotation_field() |
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section_names: AnnotationList[SectionName] = annotation_field() |
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def example_to_document( |
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example: Dict[str, Any], |
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) -> ScientificPapersDocument: |
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"""Convert a Huggingface Scientific Papers example to a PIE document.""" |
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document = ScientificPapersDocument( |
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text=example["article"], |
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) |
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document.abstract.append(AbstractiveSummary(text=example["abstract"])) |
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document.section_names.extend( |
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[SectionName(text=section_name) for section_name in example["section_names"].split("\n")] |
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) |
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return document |
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def document_to_example(doc: ScientificPapersDocument) -> Dict[str, Any]: |
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"""Convert a PIE document to a Huggingface Scientific Papers example.""" |
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example = { |
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"article": doc.text, |
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"abstract": doc.abstract[0].text, |
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"section_names": "\n".join([section_name.text for section_name in doc.section_names]), |
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} |
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return example |
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class ScientificPapersConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Scientific Papers.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Scientific Papers. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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class ScientificPapers(GeneratorBasedBuilder): |
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DOCUMENT_TYPE = ScientificPapersDocument |
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BASE_DATASET_PATH = "scientific_papers" |
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BASE_DATASET_REVISION = "14c5296f2d707630f5835c9da59dcaddeea19b20" |
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BUILDER_CONFIGS = [ |
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ScientificPapersConfig( |
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name="arxiv", |
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version=datasets.Version("1.1.1"), |
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description="Scientific Papers dataset - ArXiv variant", |
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), |
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ScientificPapersConfig( |
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name="pubmed", |
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version=datasets.Version("1.1.1"), |
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description="Scientific Papers dataset - PubMed variant", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "arxiv" |
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def _generate_document(self, example, **kwargs): |
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return example_to_document(example) |
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def _generate_example(self, document, **kwargs): |
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return document_to_example(document) |
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