Delete loading script

opus_memat.py

@@ -1,88 +0,0 @@
-# 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.
-"""Xhosa-English parallel corpora, """
-
-
-import os
-
-import datasets
-
-
-_CITATION = """\
-J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th\
- International Conference on Language Resources and Evaluation (LREC 2012)
-
-"""
-
-_DESCRIPTION = """\
-Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation\
- between ixiXhosa and English, with a focus on the medical domain."""
-
-
-_URLs = {"train": "https://object.pouta.csc.fi/OPUS-memat/v1/moses/en-xh.txt.zip"}
-
-
-class OpusMemat(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="xh-en", version=VERSION, description="Xhosa-English parallel corpora")
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"translation": datasets.features.Translation(languages=tuple(self.config.name.split("-")))}
-            ),
-            supervised_keys=None,
-            homepage="http://opus.nlpl.eu/memat.php",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URLs)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "source_file": os.path.join(data_dir["train"], "memat.en-xh.xh"),
-                    "target_file": os.path.join(data_dir["train"], "memat.en-xh.en"),
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, source_file, target_file, split):
-        """This function returns the examples in the raw (text) form."""
-        with open(source_file, encoding="utf-8") as f:
-            source_sentences = f.read().split("\n")
-        with open(target_file, encoding="utf-8") as f:
-            target_sentences = f.read().split("\n")
-
-        assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
-            len(source_sentences),
-            len(target_sentences),
-            source_file,
-            target_file,
-        )
-
-        source, target = tuple(self.config.name.split("-"))
-        for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
-            result = {"translation": {source: l1, target: l2}}
-            yield idx, result