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import json
import datasets
import pandas as pd
_CITATION = """"""
_DESCRIPTION = """"""
_LICENSE = "CC-BY-SA-4.0"
# _URL = "https://github.com/boostcampaitech2/data-annotation-nlp-level3-nlp-14"
_DATA_URLS = {
"train": "https://huggingface.co/datasets/raki-1203/ai_hub_summarization/resolve/main/train_dict.json",
"valid": "https://huggingface.co/datasets/raki-1203/ai_hub_summarization/resolve/main/valid_dict.json",
"test": "https://huggingface.co/datasets/raki-1203/ai_hub_summarization/resolve/main/test_dict.json",
}
_VERSION = "0.0.0"
class AiHubSummarizationConfig(datasets.BuilderConfig):
def __init__(self, data_url, **kwargs):
super().__init__(version=datasets.Version(_VERSION), **kwargs)
self.data_url = data_url
class AiHubSummarization(datasets.GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "ai_hub_summarization"
BUILDER_CONFIGS = [
AiHubSummarizationConfig(
name="ai_hub_summarization",
data_url=_DATA_URLS,
description=_DESCRIPTION,
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"data_name": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"doc_name": datasets.Value("string"),
"passage": datasets.Value("string"),
"abstract_summary": datasets.Value("string"),
}
),
license=_LICENSE,
citation=_CITATION,
supervised_keys=None,
)
def _split_generators(self, dl_manager):
""" Returns SplitGenerators. """
data_file = dl_manager.download_and_extract(self.config.data_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": data_file["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": data_file["valid"],
"split": "valid",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": data_file["test"],
"split": "test",
},
),
]
def _generate_examples(self, data_file: str, split: str):
""" Yields examples. """
with open(data_file, newline='', encoding="UTF-8") as f:
json_file = json.load(f)
df = pd.DataFrame(json_file)
for idx, row in df.iterrows():
features = {
"data_name": row['data_name'],
"doc_id": row['doc_id'],
"doc_name": row['doc_name'],
"passage": row['passage'],
"abstract_summary": row['abstract_summary'],
}
yield idx, features
idx += 1
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