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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """
@inproceedings{palen-michel-lignos-2023-lr,
author = {Palen-Michel, Chester and Lignos, Constantine},
title = {LR - Sum: Summarization for Less-Resourced Languages},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
year = {2023},
publisher = {Association for Computational Linguistics},
address = {Toronto, Canada},
doi = {10.18653/v1/2023.findings-acl.427},
pages = {6829--6844},
}
"""
_LOCAL = False
_LANGUAGES = ["ind", "khm", "lao", "mya", "tha", "vie"]
_DATASETNAME = "lr_sum"
_DESCRIPTION = """
LR-Sum is a news abstractive summarization dataset focused on low-resource languages. It contains human-written summaries
for 39 languages and the data is based on the Multilingual Open Text corpus
(ultimately derived from the Voice of America website).
"""
_HOMEPAGE = "https://huggingface.co/datasets/bltlab/lr-sum"
_LICENSE = Licenses.CC_BY_4_0.value
_URL = "https://huggingface.co/datasets/bltlab/lr-sum"
_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class LRSumDataset(datasets.GeneratorBasedBuilder):
"""Dataset of article-summary pairs for different low-resource languages."""
# Config to load individual datasets per language
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_{lang}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for {lang} language",
schema="source",
subset_id=f"{_DATASETNAME}_{lang}",
)
for lang in _LANGUAGES
] + [
SEACrowdConfig(
name=f"{_DATASETNAME}_{lang}_seacrowd_t2t",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for {lang} language",
schema="seacrowd_t2t",
subset_id=f"{_DATASETNAME}_{lang}",
)
for lang in _LANGUAGES
]
# Config to load all datasets
BUILDER_CONFIGS.extend(
[
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for all languages",
schema="source",
subset_id=_DATASETNAME,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_t2t",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for all languages",
schema="seacrowd_t2t",
subset_id=_DATASETNAME,
),
]
)
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"summary": datasets.Value("string"),
"text": 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."""
# dl_manager not used since dataloader uses HF 'load_dataset'
return [
datasets.SplitGenerator(name=split, gen_kwargs={"split": split._name})
for split in (
datasets.Split.TRAIN,
datasets.Split.VALIDATION,
datasets.Split.TEST,
)
]
def _load_hf_data_from_remote(self, lang: str, split: str) -> datasets.DatasetDict:
"""Load dataset from HuggingFace."""
hf_remote_ref = "/".join(_URL.split("/")[-2:])
return datasets.load_dataset(hf_remote_ref, lang, split=split)
def _generate_examples(self, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
lr_sum_datasets = []
lang = self.config.subset_id.split("_")[-1]
if lang in _LANGUAGES:
lr_sum_datasets.append(self._load_hf_data_from_remote(lang, split))
else:
for lang in _LANGUAGES:
lr_sum_datasets.append(self._load_hf_data_from_remote(lang, split))
index = 0
for lang_subset in lr_sum_datasets:
for row in lang_subset:
if self.config.schema == "source":
example = row
elif self.config.schema == "seacrowd_t2t":
example = {
"id": str(index),
"text_1": row["text"],
"text_2": row["summary"],
"text_1_name": "document",
"text_2_name": "summary",
}
yield index, example
index += 1
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