Delete loading script

miracl-japanese-small-corpus.py

@@ -1,75 +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.
-
-import json
-import datasets
-
-_CITATION = '''
-'''
-
-_DESCRIPTION = 'dataset load script for MIRACL Japanese Small Corpus'
-
-_DATASET_URLS = {
-    'train': 'https://huggingface.co/datasets/mpkato/miracl-japanese-small-corpus/resolve/main/miracl-japanese-small-docs.jsonl.gz'
-}
-
-
-class MIRACLJapaneseSmallCorpus(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            version=datasets.Version('1.0.0'),
-            description=f'MIRACL Japanese Small dataset.'
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features({
-            'docid': datasets.Value('string'), 
-            'title': datasets.Value('string'),
-            'text': datasets.Value('string'),
-        })
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage='',
-            # License for the dataset if available
-            license='',
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
-
-        splits = [
-            datasets.SplitGenerator(
-                name='train',
-                gen_kwargs={
-                    'filepath': downloaded_files['train'],
-                },
-            ),
-        ]
-        return splits
-
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as f:
-            for line in f:
-                data = json.loads(line)
-                yield data['docid'], data