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README.md

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+# PIE Dataset Card for "scientific_papers"
+
+This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
+[scientific_papers Huggingface dataset loading script](https://huggingface.co/datasets/scientific_papers).
+
+## Data Schema
+
+The document type for this dataset is `ScientificPapersDocument` which defines the following data fields:
+
+- `text` (str)
+
+and the following annotation layers:
+
+- `abstract` (annotation type: `AbstractiveSummary`, target: `None`)
+- `section_names` (annotation type: `SectionName`, targets: `None`)

requirements.txt

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+pie-datasets>=0.8.0,<0.9.0
+pie-modules>=0.8.2,<0.9.0

scientific_papers.py

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