Delete loading script

me_q_sum.py

@@ -1,122 +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.
-"""Covid Dialog dataset in English and Chinese"""
-
-
-import copy
-import os
-import re
-import textwrap
-
-import datasets
-
-
-# BibTeX citation
-_CITATION = """\
-    @Inproceedings{MeQSum,
-    author = {Asma {Ben Abacha} and Dina Demner-Fushman},
-    title = {On the Summarization of Consumer Health Questions},
-    booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019,
-    Florence, Italy, July 28th - August 2},
-    year = {2019},
-    abstract = {Question understanding is one of the main challenges in question answering. In real world applications,
-    users often submit natural language questions that are longer than needed and include peripheral information that
-    increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this
-    paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of
-    1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art
-    neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets
-    improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional
-    models on this task, with a ROUGE-1 score of 44.16%. We also present a detailed error analysis and discuss
-    directions for improvement that are specific to question summarization.}}
-"""
-
-# Official description of the dataset
-_DESCRIPTION = textwrap.dedent(
-    """
-    From "On the Summarization of Consumer Health Questions" (Abacha et al.), MeQSum is a corpus of 1,000 summarized
-    consumer health questions.
-
-    The following is an example from the dataset:
-
-    Question:
-    SUBJECT: inversion of long arm chromasome7 MESSAGE: My son has been diagnosed with inversion of long arm
-    chromasome 7 and down syndrome . please could you give me information on the chromasome 7 please because
-    our doctors have not yet mentioned it
-
-    Summary:
-    Where can I find information on chromosome 7?
-    """
-)
-
-# Link to an official homepage for the dataset here
-_HOMEPAGE = "https://worksheets.codalab.org/rest/bundles/0xd98a53314314445b96b4d703bb2d8c8c/contents/blob/"
-
-_LICENSE = ""
-
-
-import datasets
-import os
-import json
-
-
-class MeQSum(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="default", version=datasets.Version("1.0.0"), description=_DESCRIPTION)]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "query": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=f"Covid Dialogue dataset, as preprocessed and shuffled in HELM",
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        test_target = dl_manager.download("test.source")
-        test_source = dl_manager.download("test.source")
-        train_source = dl_manager.download("train.source")
-        train_target = dl_manager.download("train.target")
-        val_source = dl_manager.download("val.source")
-        val_target = dl_manager.download("val.target")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"target": train_target, "source": train_source},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"target": val_target, "source": val_source},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"target": test_target, "source": test_source},
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, source, target):
-        with open(source, encoding="utf-8") as f_source:
-            with open(target, encoding="utf-8") as f_target:
-                for idx, (s, t) in enumerate(zip(f_source, f_target)):
-                    yield idx, {"query": s, "answer": t}