V0

README.md

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+# PHEE dataset
+
+This dataset is port of https://github.com/ZhaoyueSun/PHEE,
+the data used in: [``PHEE: A Dataset for Pharmacovigilance Event Extraction from Text``](https://arxiv.org/abs/2210.12560/)

phee.py

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+"""PHEE: A Dataset for Pharmacovigilance Event Extraction from Text"""
+
+
+import json
+
+import datasets
+from datasets.tasks import LanguageModeling
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@misc{sun2022phee,
+      title={PHEE: A Dataset for Pharmacovigilance Event Extraction from Text}, 
+      author={Zhaoyue Sun and Jiazheng Li and Gabriele Pergola and Byron C. Wallace and Bino John and Nigel Greene and Joseph Kim and Yulan He},
+      year={2022},
+      eprint={2210.12560},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+Data and Code for [``PHEE: A Dataset for Pharmacovigilance Event Extraction from Text``](https://arxiv.org/abs/2210.12560/)\
+"""
+
+_URL = "https://raw.githubusercontent.com/ZhaoyueSun/PHEE/ceea192bc1f1da306980c39e53767176b1f8caec/data/json/"
+_URLS = {
+    "train": _URL + "train.json",
+    "test": _URL + "test.json",
+    "dev": _URL + "dev.json",
+}
+
+
+class PHEEConfig(datasets.BuilderConfig):
+    """BuilderConfig for PHEE."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for PHEE.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PHEEConfig, self).__init__(**kwargs)
+
+
+class PHEE(datasets.GeneratorBasedBuilder):
+    """PHEE: A Dataset for Pharmacovigilance Event Extraction from Text"""
+
+    BUILDER_CONFIGS = [
+        PHEEConfig(
+            name="json",
+            version=datasets.Version("1.0.0", ""),
+            description="processed structured data",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            homepage="https://github.com/ZhaoyueSun/PHEE",
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "is_mult_event": datasets.Value("bool"),
+                    "annotations": [
+                        {
+                            "events": [
+                                {
+                                    "event_id": datasets.Value("string"),
+                                    "event_type": datasets.Value("string"),
+                                    "event_data": datasets.Value("string"),
+                                }
+                            ]
+                        }
+                    ],
+                }
+            ),
+            citation=_CITATION,
+            task_templates=[
+                LanguageModeling(
+                    text_column="context",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples parsed from json."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            for key, line in enumerate(f):
+                obs = json.loads(line)
+                yield key, {
+                    "id": obs["id"],
+                    "context": obs["context"],
+                    "is_mult_event": obs["is_mult_event"],
+                    "annotations": [
+                        {
+                            "events": [
+                                {
+                                    "event_id": event["event_id"],
+                                    "event_type": event["event_type"],
+                                    "event_data": json.dumps(event),
+                                }
+                            for event in annotation["events"]]
+                        }
+                    for annotation in obs["annotations"]],
+                }