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Commit from https://github.com/huggingface/datasets/pie/commit/44e2b49f756ae55906addbd4cbcb339698ea0e7c

dummy/all/1.0.0/dummy_data.zip

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+size 1671

dummy/bag/1.0.0/dummy_data.zip

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dummy/bfh/1.0.0/dummy_data.zip

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dummy/bgh/1.0.0/dummy_data.zip

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dummy/bpatg/1.0.0/dummy_data.zip

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dummy/bsg/1.0.0/dummy_data.zip

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dummy/bverfg/1.0.0/dummy_data.zip

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dummy/bverwg/1.0.0/dummy_data.zip

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german_legal_entity_recognition.py

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+from dataclasses import dataclass
+
+import datasets
+import pytorch_ie.data.builder
+from pytorch_ie.annotations import LabeledSpan
+from pytorch_ie.core import AnnotationList, annotation_field
+from pytorch_ie.documents import TextDocument
+from pytorch_ie.utils.span import tokens_and_tags_to_text_and_labeled_spans
+
+_VERSION = "1.0.0"
+_COURTS = ["bag", "bfh", "bgh", "bpatg", "bsg", "bverfg", "bverwg"]
+_COURTS_FILEPATHS = {court: f"{court}.conll" for court in _COURTS}
+_ALL = "all"
+
+
+class GermanLegalEntityRecognitionConfig(datasets.BuilderConfig):
+    def __init__(self, *args, courts=None, **kwargs):
+        super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
+        self.courts = courts
+
+    @property
+    def filepaths(self):
+        return [_COURTS_FILEPATHS[court] for court in self.courts]
+
+
+@dataclass
+class GermanLegalEntityRecognitionDocument(TextDocument):
+    entities: AnnotationList[LabeledSpan] = annotation_field(target="text")
+
+
+class GermanLegalEntityRecognition(pytorch_ie.data.builder.GeneratorBasedBuilder):
+    DOCUMENT_TYPE = GermanLegalEntityRecognitionDocument
+
+    BASE_DATASET_PATH = "german_legal_entity_recognition"
+
+    BUILDER_CONFIGS = [
+        GermanLegalEntityRecognitionConfig(
+            name=court, courts=[court], description=f"Court. {court}."
+        )
+        for court in _COURTS
+    ] + [
+        GermanLegalEntityRecognitionConfig(
+            name=_ALL, courts=_COURTS, description="All courts included."
+        )
+    ]
+    BUILDER_CONFIG_CLASS = GermanLegalEntityRecognitionConfig
+    DEFAULT_CONFIG_NAME = _ALL  # type: ignore
+
+    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 = GermanLegalEntityRecognitionDocument(text=text, id=doc_id)
+
+        for span in sorted(ner_spans, key=lambda span: span.start):
+            document.entities.append(span)
+
+        return document