Datasets:
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um005.py
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# coding=utf-8
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# Copyright 2020 HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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import os
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import datasets
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_DESCRIPTION = """\
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UMC005 English-Urdu is a parallel corpus of texts in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation.
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The texts come from four different sources:
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- Quran
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- Bible
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- Penn Treebank (Wall Street Journal)
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- Emille corpus
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The authors provide the religious texts of Quran and Bible for direct download. Because of licensing reasons, Penn and Emille texts cannot be redistributed freely. However, if you already hold a license for the original corpora, we are able to provide scripts that will recreate our data on your disk. Our modifications include but are not limited to the following:
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- Correction of Urdu translations and manual sentence alignment of the Emille texts.
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- Manually corrected sentence alignment of the other corpora.
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- Our data split (training-development-test) so that our published experiments can be reproduced.
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- Tokenization (optional, but needed to reproduce our experiments).
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- Normalization (optional) of e.g. European vs. Urdu numerals, European vs. Urdu punctuation, removal of Urdu diacritics.
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"""
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_HOMEPAGE_URL = "https://ufal.mff.cuni.cz/umc/005-en-ur/"
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_URL = "https://ufal.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip"
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_CITATION = """\
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@unpublished{JaZeWordOrderIssues2011,
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author = {Bushra Jawaid and Daniel Zeman},
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title = {Word-Order Issues in {English}-to-{Urdu} Statistical Machine Translation},
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year = {2011},
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journal = {The Prague Bulletin of Mathematical Linguistics},
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number = {95},
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institution = {Univerzita Karlova},
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address = {Praha, Czechia},
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issn = {0032-6585},
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}
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"""
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_ALL = "all"
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_VERSION = "1.0.0"
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_SOURCES = ["bible", "quran"]
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_SOURCES_FILEPATHS = {
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s: {
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"train": {"urdu": "train.ur", "english": "train.en"},
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"dev": {"urdu": "dev.ur", "english": "dev.en"},
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"test": {"urdu": "test.ur", "english": "test.en"},
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}
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for s in _SOURCES
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}
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class UM005Config(datasets.BuilderConfig):
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def __init__(self, *args, sources=None, **kwargs):
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super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
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self.sources = sources
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@property
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def language_pair(self):
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return ("ur", "en")
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class UM005(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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UM005Config(name=source, sources=[source], description=f"Source: {source}.") for source in _SOURCES
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] + [
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UM005Config(
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name=_ALL,
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sources=_SOURCES,
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description="All sources included: bible, quran",
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)
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]
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BUILDER_CONFIG_CLASS = UM005Config
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DEFAULT_CONFIG_NAME = _ALL
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"translation": datasets.Translation(languages=self.config.language_pair),
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},
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),
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supervised_keys=None,
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homepage=_HOMEPAGE_URL,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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path = dl_manager.download_and_extract(_URL)
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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={"datapath": path, "datatype": "train"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"datapath": path, "datatype": "dev"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"datapath": path, "datatype": "test"},
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),
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]
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def _generate_examples(self, datapath, datatype):
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if datatype == "train":
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ur_file = "train.ur"
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en_file = "train.en"
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elif datatype == "dev":
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ur_file = "dev.ur"
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en_file = "dev.en"
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elif datatype == "test":
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ur_file = "test.ur"
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en_file = "test.en"
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else:
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raise Exception("Invalid dataype. Try one of: dev, train, test")
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for source in self.config.sources:
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urdu_path = os.path.join(datapath, source, ur_file)
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english_path = os.path.join(datapath, source, en_file)
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with open(urdu_path, encoding="utf-8") as u, open(english_path, encoding="utf-8") as e:
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for sentence_counter, (x, y) in enumerate(zip(u, e)):
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x = x.strip()
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y = y.strip()
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id_ = f"{source}-{sentence_counter}"
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yield id_, {
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"id": id_,
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"translation": {"ur": x, "en": y},
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}
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