Delete loading script

aquamuse.py

@@ -1,154 +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.
-"""AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""
-
-
-import os
-from os import listdir
-from os.path import isfile, join
-
-import tensorflow as tf
-
-import datasets
-
-
-_CITATION = """\
-@misc{kulkarni2020aquamuse,
-      title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
-      author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
-      year={2020},
-      eprint={2010.12694},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""
-
-_HOMEPAGE = "https://github.com/google-research-datasets/aquamuse"
-
-_LICENSE = ""
-
-zipped_data_url = "https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip"
-
-
-class Aquamuse(datasets.GeneratorBasedBuilder):
-    """Dataset for Query-based Multi-Document Summarization"""
-
-    VERSION = datasets.Version("2.3.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="abstractive", version=VERSION, description="Abstractive query-based multi-document summarization"
-        ),
-        datasets.BuilderConfig(
-            name="extractive", version=VERSION, description="Extractive query-based multi-document summarization"
-        ),
-    ]
-
-    # DEFAULT_CONFIG_NAME = "abstractive"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "query": datasets.Value("string"),
-                "input_urls": datasets.Sequence(datasets.Value("string")),
-                "target": datasets.Value("string"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        if self.config.name == "abstractive":
-            data_dir = dl_manager.download_and_extract(zipped_data_url)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/abstractive/train/"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/abstractive/test/"),
-                        "split": "test",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/abstractive/dev/"),
-                        "split": "dev",
-                    },
-                ),
-            ]
-
-        else:
-            data_dir = dl_manager.download_and_extract(zipped_data_url)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/extractive/train/"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/extractive/test/"),
-                        "split": "test",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/extractive/dev/"),
-                        "split": "dev",
-                    },
-                ),
-            ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        filepath = [join(filepath, f) for f in listdir(filepath) if isfile(join(filepath, f))]
-        filepath = sorted(filepath)
-        raw_dataset = tf.data.TFRecordDataset(filepath)
-        for id_, raw_record in enumerate(raw_dataset):
-            example = tf.train.Example()
-            example.ParseFromString(raw_record.numpy())
-            yield id_, {
-                "query": example.features.feature["query"].bytes_list.value[0].decode(),
-                "input_urls": example.features.feature["input_urls"].bytes_list.value[0].decode().split("<EOD>"),
-                "target": example.features.feature["target"].bytes_list.value[0].decode(),
-            }