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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Tasks |
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from seacrowd.utils import schemas |
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import json |
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_CITATION = """\ |
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@inproceedings{koto2020liputan6, |
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title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization}, |
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author={Koto, Fajri and Lau, Jey Han and Baldwin, Timothy}, |
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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}, |
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pages={598--608}, |
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year={2020} |
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} |
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""" |
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_LOCAL = False |
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_LANGUAGES = ["ind"] |
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_DATASETNAME = "liputan6" |
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_DESCRIPTION = """ |
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A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs. |
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""" |
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_HOMEPAGE = "https://github.com/fajri91/sum_liputan6" |
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_LICENSE = "CC-BY-SA 4.0" |
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_URLS = { |
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_DATASETNAME: "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/downstream_task/downstream_task_datasets.zip", |
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} |
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_SUPPORTED_TASKS = [Tasks.SUMMARIZATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class Liputan6(datasets.GeneratorBasedBuilder): |
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"""A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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TYPE_LIST = ['canonical', 'xtreme'] |
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BUILDER_CONFIGS = ( |
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[ |
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SEACrowdConfig( |
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name="liputan6_{fold_name}_source".format(fold_name=i), |
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version=_SOURCE_VERSION, |
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description="liputan6 source schema", |
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schema="source", |
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subset_id="liputan6_{fold_name}".format(fold_name=i), |
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) for i in TYPE_LIST |
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] |
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+ |
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[ |
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SEACrowdConfig( |
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name="liputan6_{fold_name}_seacrowd_t2t".format(fold_name=i), |
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version=_SEACROWD_VERSION, |
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description="liputan6 Nusantara schema", |
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schema="seacrowd_t2t", |
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subset_id="liputan6_{fold_name}".format(fold_name=i), |
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) for i in TYPE_LIST |
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] |
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) |
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DEFAULT_CONFIG_NAME = "liputan6_canonical_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"document": datasets.Value("string"), |
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"id": datasets.Value("string"), |
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"summary": datasets.Value("string") |
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} |
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) |
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elif self.config.schema == "seacrowd_t2t": |
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features = schemas.text2text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _get_fold_name(self): |
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subset_id = self.config.subset_id |
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idx_fold = subset_id.index("_") |
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file_id = subset_id[(idx_fold + 1):] |
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return file_id |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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fold_name = self._get_fold_name() |
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urls = _URLS[_DATASETNAME] |
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data_dir = Path(dl_manager.download_and_extract(urls)) |
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location = { |
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"train": "IndoNLG_downstream_tasks/liputan6/{fold_name}_train.json", |
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"test": "IndoNLG_downstream_tasks/liputan6/{fold_name}_test.json", |
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"dev": "IndoNLG_downstream_tasks/liputan6/{fold_name}_dev.json" |
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} |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, location["train"].format(fold_name=fold_name)), |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, location["test"].format(fold_name=fold_name)), |
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"split": "test", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, location["dev"].format(fold_name=fold_name)), |
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"split": "dev", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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if self.config.schema == "source": |
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if "xtreme_train.json" in filepath: |
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with open(filepath) as f: |
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lines = f.read().split("{") |
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LEN = len(lines) |
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for i, line in enumerate(lines): |
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if 0 < i < LEN-1: |
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idx = line.index("}") |
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line = "{"+line[:idx+1] |
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each_data = json.loads(line) |
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ex = { |
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"id": each_data["id"], |
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"document": each_data['text'], |
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"summary": each_data['label'] |
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} |
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yield each_data["id"], ex |
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else: |
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with open(filepath) as f: |
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data = json.load(f) |
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for i, each_data in enumerate(data): |
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ex = { |
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"id": each_data["id"], |
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"document": each_data['text'], |
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"summary": each_data['label'] |
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} |
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yield each_data["id"], ex |
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elif self.config.schema == "seacrowd_t2t": |
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if "xtreme_train.json" in filepath: |
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with open(filepath) as f: |
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lines = f.read().split("{") |
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LEN = len(lines) |
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for i, line in enumerate(lines): |
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if 0 < i < LEN-1: |
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idx = line.index("}") |
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line = "{"+line[:idx+1] |
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each_data = json.loads(line) |
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ex = { |
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"id": each_data["id"], |
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"text_1": each_data['text'], |
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"text_2": each_data['label'], |
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"text_1_name": "document", |
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"text_2_name": "summary" |
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} |
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yield each_data["id"], ex |
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else: |
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with open(filepath) as f: |
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data = json.load(f) |
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for i, each_data in enumerate(data): |
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ex = { |
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"id": each_data["id"], |
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"text_1": each_data['text'], |
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"text_2": each_data['label'], |
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"text_1_name": "document", |
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"text_2_name": "summary" |
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} |
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yield each_data["id"], ex |
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