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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{bhattacharjee-etal-2023-crosssum,
author = {Bhattacharjee, Abhik and Hasan, Tahmid and Ahmad, Wasi Uddin and Li, Yuan-Fang and Kang, Yong-Bin and Shahriyar, Rifat},
title = {CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
publisher = {Association for Computational Linguistics},
year = {2023},
url = {https://aclanthology.org/2023.acl-long.143},
doi = {10.18653/v1/2023.acl-long.143},
pages = {2541--2564},
}
"""
_LOCAL = False
_LANGUAGES = ["ind", "mya", "vie"]
_DATASETNAME = "crosssum"
_DESCRIPTION = """
This is a large-scale cross-lingual summarization dataset containing article-summary samples in 1,500+ language pairs,
including pairs with the Burmese, Indonesian and Vietnamese languages. Articles in the first language are assigned
summaries in the second language.
"""
_HOMEPAGE = "https://huggingface.co/datasets/csebuetnlp/CrossSum"
_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
_URL = "https://huggingface.co/datasets/csebuetnlp/CrossSum"
_SUPPORTED_TASKS = [Tasks.CROSS_LINGUAL_SUMMARIZATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class CrossSumDataset(datasets.GeneratorBasedBuilder):
"""Dataset of cross-lingual article-summary samples."""
SUBSETS = [
"ind_mya",
"ind_vie",
"mya_ind",
"mya_vie",
"vie_mya",
"vie_ind",
]
LANG_CODE_MAPPER = {"ind": "indonesian", "mya": "burmese", "vie": "vietnamese"}
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema for {subset} subset",
schema="source",
subset_id=f"{_DATASETNAME}_{subset}",
)
for subset in SUBSETS
] + [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_seacrowd_t2t",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
schema="seacrowd_t2t",
subset_id=f"{_DATASETNAME}_{subset}",
)
for subset in SUBSETS
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_ind_mya_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"source_url": datasets.Value("string"),
"target_url": 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, split: str) -> datasets.DatasetDict:
"""Load dataset from HuggingFace."""
source_lang = self.LANG_CODE_MAPPER[self.config.subset_id.split("_")[-2]]
target_lang = self.LANG_CODE_MAPPER[self.config.subset_id.split("_")[-1]]
HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
_hf_dataset_source = datasets.load_dataset(HF_REMOTE_REF, f"{source_lang}-{target_lang}", split=split)
return _hf_dataset_source
def _generate_examples(self, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
data = self._load_hf_data_from_remote(split)
for index, row in enumerate(data):
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
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