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import os
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
from seacrowd.utils import schemas
import json
_CITATION = """\
@inproceedings{koto2020liputan6,
title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
author={Koto, Fajri and Lau, Jey Han and Baldwin, Timothy},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
pages={598--608},
year={2020}
}
"""
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_DATASETNAME = "liputan6"
_DESCRIPTION = """
A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs.
"""
_HOMEPAGE = "https://github.com/fajri91/sum_liputan6"
_LICENSE = "CC-BY-SA 4.0"
_URLS = {
_DATASETNAME: "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/downstream_task/downstream_task_datasets.zip",
}
_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class Liputan6(datasets.GeneratorBasedBuilder):
"""A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
TYPE_LIST = ['canonical', 'xtreme']
BUILDER_CONFIGS = (
[
SEACrowdConfig(
name="liputan6_{fold_name}_source".format(fold_name=i),
version=_SOURCE_VERSION,
description="liputan6 source schema",
schema="source",
subset_id="liputan6_{fold_name}".format(fold_name=i),
) for i in TYPE_LIST
]
+
[
SEACrowdConfig(
name="liputan6_{fold_name}_seacrowd_t2t".format(fold_name=i),
version=_SEACROWD_VERSION,
description="liputan6 Nusantara schema",
schema="seacrowd_t2t",
subset_id="liputan6_{fold_name}".format(fold_name=i),
) for i in TYPE_LIST
]
)
DEFAULT_CONFIG_NAME = "liputan6_canonical_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"document": datasets.Value("string"),
"id": datasets.Value("string"),
"summary": 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 _get_fold_name(self):
subset_id = self.config.subset_id
idx_fold = subset_id.index("_")
file_id = subset_id[(idx_fold + 1):]
return file_id
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
fold_name = self._get_fold_name()
urls = _URLS[_DATASETNAME]
data_dir = Path(dl_manager.download_and_extract(urls))
location = {
"train": "IndoNLG_downstream_tasks/liputan6/{fold_name}_train.json",
"test": "IndoNLG_downstream_tasks/liputan6/{fold_name}_test.json",
"dev": "IndoNLG_downstream_tasks/liputan6/{fold_name}_dev.json"
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, location["train"].format(fold_name=fold_name)),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, location["test"].format(fold_name=fold_name)),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, location["dev"].format(fold_name=fold_name)),
"split": "dev",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
if self.config.schema == "source":
if "xtreme_train.json" in filepath:
with open(filepath) as f:
lines = f.read().split("{")
LEN = len(lines)
for i, line in enumerate(lines):
if 0 < i < LEN-1:
idx = line.index("}")
line = "{"+line[:idx+1]
each_data = json.loads(line)
ex = {
"id": each_data["id"],
"document": each_data['text'],
"summary": each_data['label']
}
yield each_data["id"], ex
else:
with open(filepath) as f:
data = json.load(f)
for i, each_data in enumerate(data):
ex = {
"id": each_data["id"],
"document": each_data['text'],
"summary": each_data['label']
}
yield each_data["id"], ex
elif self.config.schema == "seacrowd_t2t":
if "xtreme_train.json" in filepath:
with open(filepath) as f:
lines = f.read().split("{")
LEN = len(lines)
for i, line in enumerate(lines):
if 0 < i < LEN-1:
idx = line.index("}")
line = "{"+line[:idx+1]
each_data = json.loads(line)
ex = {
"id": each_data["id"],
"text_1": each_data['text'],
"text_2": each_data['label'],
"text_1_name": "document",
"text_2_name": "summary"
}
yield each_data["id"], ex
else:
with open(filepath) as f:
data = json.load(f)
for i, each_data in enumerate(data):
ex = {
"id": each_data["id"],
"text_1": each_data['text'],
"text_2": each_data['label'],
"text_1_name": "document",
"text_2_name": "summary"
}
yield each_data["id"], ex
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