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import os
import pickle
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """\
@inproceedings{
ladhak-wiki-2020,
title={WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization},
author={Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown},
booktitle={Findings of EMNLP, 2020},
year={2020}
}
"""
_DATASETNAME = "wikilingua"
_DESCRIPTION = """\
We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive
summarization systems. We extract article and summary pairs in 18 languages from WikiHow12, a high quality,
collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard
article summary alignments across languages by aligning the images that are used to describe each how-to step in an
article.
"""
_HOMEPAGE = "https://github.com/esdurmus/Wikilingua"
_LANGUAGES = ["ind"]
_LICENSE = "CC-BY-NC-SA 3.0"
_LOCAL = False
_URLS = {
_DATASETNAME: "https://drive.google.com/u/0/uc?id=1PGa8j1_IqxiGTc3SU6NMB38sAzxCPS34&export=download"
}
_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class Wikilingua(datasets.GeneratorBasedBuilder):
"""
The dataset includes 47,511 articles from WikiHow. Extracted gold-standard article-summary alignments across
languages by aligning the images that are used to describe each how-to step in an article.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="wikilingua_source",
version=SOURCE_VERSION,
description="wikilingua source schema",
schema="source",
subset_id="wikilingua",
),
SEACrowdConfig(
name="wikilingua_seacrowd_t2t",
version=SEACROWD_VERSION,
description="wikilingua Nusantara schema",
schema="seacrowd_t2t",
subset_id="wikilingua",
),
]
DEFAULT_CONFIG_NAME = "wikilingua_source"
def _info(self) -> datasets.DatasetInfo:
features = []
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("int64"),
"link": datasets.Value("string"),
"main_point": datasets.Value("string"),
"summary": datasets.Value("string"),
"document": datasets.Value("string"),
"english_section_name": datasets.Value("string"),
"english_url": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_t2t":
features = schemas.text2text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir),
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
with open(filepath, "rb") as file:
indonesian_docs = pickle.load(file)
_id = 1
for key_link, articles in indonesian_docs.items():
for main_point, items in articles.items():
example = {"id": _id, "link": key_link, "main_point": main_point, "summary": items["summary"], "document": items["document"], "english_section_name": items["english_section_name"], "english_url": items["english_url"]}
yield _id, example
_id += 1
elif self.config.schema == "seacrowd_t2t":
with open(filepath, "rb") as file:
indonesian_docs = pickle.load(file)
_id = 1
for key_link, articles in indonesian_docs.items():
for main_point, items in articles.items():
example = {"id": _id, "text_1": items["document"], "text_2": items["summary"], "text_1_name": "document", "text_2_name": "summary"}
yield _id, example
_id += 1
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