First version of the re-medical-annotations dataset.

README.md

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+# Dataset Card for re-medical-annotations
+
+## Dataset Description
+
+### Dataset Summary
+
+HuggingFace Dataset from the Inception Medical Annotations project.
+
+This dataset can be used locally with any archive downloaded from Inception that contains relation annotations.
+
+**Example**: load the dataset from the "RE Temporality POC"
+
+```
+import datasets
+
+ds = datasets.load_dataset(
+    data_dir=<Inception Archive path>,
+    labels = ["bound"],
+)
+```
+
+## Dataset Structure
+
+### Data Fields
+
+- `text (str)`: text of the sentence
+- `subj_start (int)`: start char of the relation subject mention
+- `subj_end (int)`: end char of the relation subject mention, exclusive
+- `subj_type (str)`: NER label of the relation subject
+- `obj_start (int)`: start char of the relation object mention
+- `obj_end (int)`: end char of the relation object mention, exclusive
+- `obj_type (str)`: NER label of the relation object
+- `relation (str)`: the relation label of this instance

dataset.py

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+"""RE Dataset, Arkhn style."""
+import itertools
+import os
+import zipfile
+from dataclasses import dataclass
+from glob import glob
+from pathlib import Path
+from typing import Optional
+
+import datasets
+from cassis import Cas, load_cas_from_xmi, load_typesystem
+
+# You can copy an official description
+_DESCRIPTION = (
+    "This dataset is designed to solve the great task of Relation Extraction and "
+    "is crafted with a lot of care."
+)
+
+SENTENCE_CAS = "de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Sentence"
+CUSTOM_RELATION_CAS = "custom.Relation"
+DOCUMENT_METADATA = "de.tudarmstadt.ukp.dkpro.core.api.metadata.type.DocumentMetaData"
+CUSTOM_SPAN = "custom.Span"
+
+
+@dataclass
+class ReMedicalAnnotationsConfig(datasets.BuilderConfig):
+    """BuilderConfig for ReMedicalAnnotations dataset."""
+
+    labels: Optional[list[str]] = None
+
+
+class ReMedicalAnnotations(datasets.GeneratorBasedBuilder):
+    """This dataset is designed to solve the great task of Relation Extraction and is crafted
+    with a lot of care."""
+
+    BUILDER_CONFIG_CLASS = ReMedicalAnnotationsConfig
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "subj_start": datasets.Value("int32"),
+                    "subj_end": datasets.Value("int32"),
+                    "subj_type": datasets.Value("string"),
+                    "obj_start": datasets.Value("int32"),
+                    "obj_end": datasets.Value("int32"),
+                    "obj_type": datasets.Value("string"),
+                    "relation": datasets.ClassLabel(names=["no_relation"] + self.config.labels),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.extract(self.config.data_dir)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "all",
+                },
+            )
+        ]
+
+    @staticmethod
+    def get_cas_objects(filepath: str):
+        cas_objects: list[Cas] = []
+
+        curation_path = os.path.join(filepath, "curation")
+        for zip_subset_path in sorted(glob(curation_path + "/**/*.zip")):
+            with zipfile.ZipFile(zip_subset_path) as zip_subset:
+                subset_folder = str(Path(zip_subset_path).parent)
+                zip_subset.extractall(subset_folder)
+                with open(glob(subset_folder + "/*.xml")[0], "rb") as f:
+                    typesystem = load_typesystem(f)
+                with open(glob(subset_folder + "/*.xmi")[0], "rb") as f:
+                    cas = load_cas_from_xmi(f, typesystem=typesystem)
+                cas_objects.append(cas)
+
+        return cas_objects
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath: str, split: str):
+        """Generate RE examples from an unzipped Inception dataset."""
+        key = 0
+        for cas in self.get_cas_objects(filepath=filepath):
+            examples = cas.select(SENTENCE_CAS)
+            for example in examples:
+                offset = (
+                    cas.select(DOCUMENT_METADATA)[0]
+                    .get_covered_text()
+                    .find(example.get_covered_text())
+                )
+
+                # retrieve all the relations as tuples (dependant, governor) with the span ids
+                # and the corresponding relation label
+                relations = {}
+                for relation in cas.select_covered(CUSTOM_RELATION_CAS, example):
+                    relations[(relation.Dependent.xmiID, relation.Governor.xmiID)] = relation.label
+
+                # Create all possible combinations of 2 entities (we keep in undirected for now)
+                entities = cas.select_covered(CUSTOM_SPAN, example)
+                combinations = itertools.combinations(entities, 2)
+                for ent1, ent2 in combinations:
+                    if (ent1.xmiID, ent2.xmiID) in relations.keys():
+                        relation = relations[(ent1.xmiID, ent2.xmiID)]
+                    else:
+                        relation = "no_relation"
+                    yield key, {
+                        "text": example.get_covered_text(),
+                        "subj_start": ent1.begin - offset,
+                        "subj_end": ent1.end - offset,
+                        "subj_type": ent1.label,
+                        "obj_start": ent2.begin - offset,
+                        "obj_end": ent2.end - offset,
+                        "obj_type": ent2.label,
+                        "relation": relation,
+                    }
+                    key += 1