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README.md

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+# PIE Dataset Card for "sciarg"
+
+This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the SciArg dataset.
+
+TODO: Since there is no respective HF dataset card for SciArg, we should all respective information here.
+
+TODO: Shortly reference the PIE-Brat dataset card.
+
+## Data Schema
+
+TODO
+
+See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the remaining annotation type definitions.
+
+## Document Converters
+
+The dataset provides document converters for the following target document types:
+
+- `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations`
+  - TODO
+- `pytorch_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions`
+  - TODO (may reference the above)
+
+See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
+definitions.

requirements.txt

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+pie-datasets>=0.4.0,<0.5.0

sciarg.py

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+from pytorch_ie.core import Document
+from pytorch_ie.documents import (
+    TextDocumentWithLabeledSpansAndBinaryRelations,
+    TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
+)
+
+from pie_datasets.builders import BratBuilder
+from pie_datasets.core.dataset import DocumentConvertersType
+from pie_datasets.document.processing import (
+    Caster,
+    Pipeline,
+    RegexPartitioner,
+    RelationArgumentSorter,
+    TextSpanTrimmer,
+)
+
+URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"
+SPLIT_PATHS = {"train": "compiled_corpus"}
+
+
+def get_common_pipeline_steps(target_document_type: type[Document]) -> dict:
+    return dict(
+        cast=Caster(
+            document_type=target_document_type,
+            field_mapping={"spans": "labeled_spans", "relations": "binary_relations"},
+        ),
+        trim_adus=TextSpanTrimmer(layer="labeled_spans"),
+        sort_symmetric_relation_arguments=RelationArgumentSorter(
+            relation_layer="binary_relations",
+            label_whitelist=["parts_of_same", "semantically_same"],
+        ),
+    )
+
+
+class SciArg(BratBuilder):
+    BASE_DATASET_PATH = "DFKI-SLT/brat"
+    BASE_DATASET_REVISION = "052163d34b4429d81003981bc10674cef54aa0b8"
+
+    # we need to add None to the list of dataset variants to support the default dataset variant
+    BASE_BUILDER_KWARGS_DICT = {
+        dataset_variant: {"url": URL, "split_paths": SPLIT_PATHS}
+        for dataset_variant in ["default", "merge_fragmented_spans", None]
+    }
+
+    @property
+    def document_converters(self) -> DocumentConvertersType:
+        if self.config.name == "default":
+            return {}
+        elif self.config.name == "merge_fragmented_spans":
+            return {
+                TextDocumentWithLabeledSpansAndBinaryRelations: Pipeline(
+                    **get_common_pipeline_steps(TextDocumentWithLabeledSpansAndBinaryRelations)
+                ),
+                TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions: Pipeline(
+                    **get_common_pipeline_steps(
+                        TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
+                    ),
+                    add_partitions=RegexPartitioner(
+                        partition_layer_name="labeled_partitions",
+                        pattern="<([^>/]+)>.*</\\1>",
+                        label_group_id=1,
+                        label_whitelist=["Title", "Abstract", "H1"],
+                        skip_initial_partition=True,
+                        strip_whitespace=True,
+                    ),
+                ),
+            }
+        else:
+            raise ValueError(f"Unknown dataset variant: {self.config.name}")