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import os
import datasets
_CITATION = """\
# (Optional) Add your citation here
"""
_DESCRIPTION = """\
NLGraph: A collection of graph-related tasks with natural language representations.
Each subset corresponds to a task (e.g., connectivity, cycle, shortest_path).
Each split contains examples stored in JSON Lines format.
"""
# Map folder name to config (subset)
TASKS = [
"connectivity",
"cycle",
"flow",
"GNN",
"hamilton",
"matching",
"shortest_path",
"topology",
]
class NLGraphConfig(datasets.BuilderConfig):
def __init__(self, task_name, **kwargs):
super().__init__(name=task_name, **kwargs)
self.task_name = task_name
class NLGraph(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NLGraphConfig(task_name=task) for task in TASKS
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"difficulty": datasets.Value("string"),
"doc_id": datasets.Value("string"),
# add more fields depending on your JSONL schema
}),
supervised_keys=None,
homepage="https://huggingface.co/datasets/huayangli/nlgraph",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_files = {
"test": dl_manager.download_and_extract(os.path.join(self.config.name, "test.jsonl")),
"train": dl_manager.download_and_extract(os.path.join(self.config.name, "train.jsonl")),
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files['train']},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_files['test']},
),
]
def _generate_examples(self, filepath):
import json
with open(filepath, "r", encoding="utf-8") as f:
for idx, line in enumerate(f):
data = json.loads(line)
yield idx, data
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