Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 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. | |
"""PANL BPPT""" | |
import os | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """\ | |
@inproceedings{id_panl_bppt, | |
author = {PAN Localization - BPPT}, | |
title = {Parallel Text Corpora, English Indonesian}, | |
year = {2009}, | |
url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf}, | |
} | |
""" | |
_DESCRIPTION = """\ | |
Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and | |
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing | |
Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, | |
Science and Technology and Sport). | |
""" | |
_HOMEPAGE = "http://digilib.bppt.go.id/sampul/p92-budiono.pdf" | |
_LICENSE = "" | |
_URLs = ["https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/BPPTIndToEngCorpusHalfM.zip"] | |
class IdPanlBpptConfig(datasets.BuilderConfig): | |
"""BuilderConfig for IdPanlBppt""" | |
def __init__(self, src_tag=None, tgt_tag=None, topics=None, **kwargs): | |
"""BuilderConfig for IdPanlBppt. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(IdPanlBpptConfig, self).__init__(**kwargs) | |
self.src_tag = src_tag | |
self.tgt_tag = tgt_tag | |
self.topics = topics | |
class IdPanlBppt(datasets.GeneratorBasedBuilder): | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
IdPanlBpptConfig( | |
name="id_panl_bppt", | |
version=VERSION, | |
description="IdPanlBppt dataset", | |
src_tag="en", | |
tgt_tag="id", | |
topics=[ | |
{"name": "Economy", "words": "150K"}, | |
{"name": "International", "words": "150K"}, | |
{"name": "Science", "words": "100K"}, | |
{"name": "Sport", "words": "100K"}, | |
], | |
), | |
] | |
BUILDER_CONFIG_CLASS = IdPanlBpptConfig | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"translation": datasets.features.Translation(languages=[self.config.src_tag, self.config.tgt_tag]), | |
"topic": datasets.features.ClassLabel(names=[topic["name"] for topic in self.config.topics]), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
my_urls = _URLs[0] | |
data_dir = dl_manager.download_and_extract(my_urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_dir": os.path.join(data_dir, "plain"), | |
"split": "train", | |
}, | |
), | |
] | |
def _generate_examples(self, data_dir, split): | |
logger.info("⏳ Generating %s examples from = %s", split, data_dir) | |
id = 0 | |
for topic in self.config.topics: | |
src_path = f"PANL-BPPT-{topic['name'][:3].upper()}-{self.config.src_tag.upper()}-{topic['words']}w.txt" | |
tgt_path = f"PANL-BPPT-{topic['name'][:3].upper()}-{self.config.tgt_tag.upper()}-{topic['words']}w.txt" | |
with open(os.path.join(data_dir, src_path), encoding="utf-8") as f1, open( | |
os.path.join(data_dir, tgt_path), encoding="utf-8" | |
) as f2: | |
src = f1.read().split("\n")[:-1] | |
tgt = f2.read().split("\n")[:-1] | |
for idx, (s, t) in enumerate(zip(src, tgt)): | |
yield id, { | |
"id": str(id), | |
"translation": {self.config.src_tag: s, self.config.tgt_tag: t}, | |
"topic": topic["name"], | |
} | |
id += 1 | |