Delete loading script

um005.py

@@ -1,145 +0,0 @@
-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-import os
-
-import datasets
-
-
-_DESCRIPTION = """\
-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.
-
-The texts come from four different sources:
-- Quran
-- Bible
-- Penn Treebank (Wall Street Journal)
-- Emille corpus
-
-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:
-
-- Correction of Urdu translations and manual sentence alignment of the Emille texts.
-- Manually corrected sentence alignment of the other corpora.
-- Our data split (training-development-test) so that our published experiments can be reproduced.
-- Tokenization (optional, but needed to reproduce our experiments).
-- Normalization (optional) of e.g. European vs. Urdu numerals, European vs. Urdu punctuation, removal of Urdu diacritics.
-"""
-_HOMEPAGE_URL = "https://ufal.mff.cuni.cz/umc/005-en-ur/"
-_URL = "https://ufal.mff.cuni.cz/umc/005-en-ur/download.php?f=umc005-corpus.zip"
-_CITATION = """\
-@unpublished{JaZeWordOrderIssues2011,
-author      = {Bushra Jawaid and Daniel Zeman},
-title       = {Word-Order Issues in {English}-to-{Urdu} Statistical Machine Translation},
-year        = {2011},
-journal     = {The Prague Bulletin of Mathematical Linguistics},
-number      = {95},
-institution = {Univerzita Karlova},
-address     = {Praha, Czechia},
-issn        = {0032-6585},
-}
-"""
-
-_ALL = "all"
-_VERSION = "1.0.0"
-_SOURCES = ["bible", "quran"]
-_SOURCES_FILEPATHS = {
-    s: {
-        "train": {"urdu": "train.ur", "english": "train.en"},
-        "dev": {"urdu": "dev.ur", "english": "dev.en"},
-        "test": {"urdu": "test.ur", "english": "test.en"},
-    }
-    for s in _SOURCES
-}
-
-
-class UM005Config(datasets.BuilderConfig):
-    def __init__(self, *args, sources=None, **kwargs):
-        super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
-        self.sources = sources
-
-    @property
-    def language_pair(self):
-        return ("ur", "en")
-
-
-class UM005(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        UM005Config(name=source, sources=[source], description=f"Source: {source}.") for source in _SOURCES
-    ] + [
-        UM005Config(
-            name=_ALL,
-            sources=_SOURCES,
-            description="All sources included: bible, quran",
-        )
-    ]
-    BUILDER_CONFIG_CLASS = UM005Config
-    DEFAULT_CONFIG_NAME = _ALL
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "translation": datasets.Translation(languages=self.config.language_pair),
-                },
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE_URL,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        path = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"datapath": path, "datatype": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"datapath": path, "datatype": "dev"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"datapath": path, "datatype": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, datapath, datatype):
-        if datatype == "train":
-            ur_file = "train.ur"
-            en_file = "train.en"
-        elif datatype == "dev":
-            ur_file = "dev.ur"
-            en_file = "dev.en"
-        elif datatype == "test":
-            ur_file = "test.ur"
-            en_file = "test.en"
-        else:
-            raise Exception("Invalid dataype. Try one of: dev, train, test")
-
-        for source in self.config.sources:
-            urdu_path = os.path.join(datapath, source, ur_file)
-            english_path = os.path.join(datapath, source, en_file)
-            with open(urdu_path, encoding="utf-8") as u, open(english_path, encoding="utf-8") as e:
-                for sentence_counter, (x, y) in enumerate(zip(u, e)):
-                    x = x.strip()
-                    y = y.strip()
-                    id_ = f"{source}-{sentence_counter}"
-                    yield id_, {
-                        "id": id_,
-                        "translation": {"ur": x, "en": y},
-                    }