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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='dev', 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"]],
}
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