| # # 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. | |
| # """A Dataset loading script for the Controlled Text Reduction dataset.""" | |
| # import datasets | |
| # from dataclasses import dataclass | |
| # from pathlib import Path | |
| # from typing import List, Tuple | |
| # import pandas as pd | |
| # import json | |
| # import gzip | |
| # import itertools | |
| # _CITATION = """""" | |
| # # _CITATION = """\ | |
| # # @inproceedings{roit2020controlled, | |
| # # title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation}, | |
| # # author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido}, | |
| # # booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, | |
| # # pages={7008--7013}, | |
| # # year={2020} | |
| # # } | |
| # # """ | |
| # _DESCRIPTION = """\ | |
| # The dataset contains document-summary pairs with document spans (referred to as "highlights"), indicating the "pre-selected" spans that lead to the creation of the summary. | |
| # The evaluation and test datasets were constructed via controlled crowdsourcing. | |
| # The train datasets were automatically generated using the summary-source proposition-level alignment model SuperPAL (Ernst et al., 2021). | |
| # """ | |
| # _HOMEPAGE = "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main" | |
| # _LICENSE = """MIT License | |
| # Copyright (c) 2022 lovodkin93 | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE.""" | |
| # # _URLs = { | |
| # # "csv": { | |
| # # "sentences": { | |
| # # "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv", | |
| # # "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv", | |
| # # "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv", | |
| # # "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv", | |
| # # }, | |
| # # "qasrl-annotations": { | |
| # # "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv", | |
| # # "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv", | |
| # # "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv", | |
| # # "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv", | |
| # # }, | |
| # # }, | |
| # # "jsonl": "https://qasrl.org/data/qasrl-gs.tar" | |
| # # } | |
| # _URLs = { | |
| # "DUC-2001-2002": { | |
| # "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv", | |
| # "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv", | |
| # "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_DUC-2001-2002.csv" | |
| # }, | |
| # "CNN-DM": { | |
| # "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_CNNDM.csv", | |
| # "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv", | |
| # "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv", | |
| # }, | |
| # } | |
| # @dataclass | |
| # class ControlledTextReductionConfig(datasets.BuilderConfig): | |
| # """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """ | |
| # data_source: str = "DUC-2001-2002" # "DUC-2001-2002" or "CNN-DM" | |
| # class ControlledTextReduction(datasets.GeneratorBasedBuilder): | |
| # """Controlled Text Reduction: dataset for the Controlled Text Reduction task (). | |
| # Each data point consists of a document, a summary, and a list of spans of the document that are the pre-selected content whose summary is the summary""" | |
| # VERSION = datasets.Version("1.0.0") | |
| # BUILDER_CONFIG_CLASS = ControlledTextReductionConfig | |
| # BUILDER_CONFIGS = [ | |
| # ControlledTextReductionConfig( | |
| # name="DUC-2001-2002", | |
| # version=VERSION, | |
| # description="This provides the Controlled Text Reduction dataset extracted from the DUC 2001-2002 Single Document Summarization benchmark", | |
| # data_source="DUC-2001-2002" | |
| # ), | |
| # ControlledTextReductionConfig( | |
| # name="CNN-DM", | |
| # version=VERSION, | |
| # description="This provides the Controlled Text Reduction dataset extracted from the CNN-DM dataset (the train split)", | |
| # data_source="CNN-DM" | |
| # ) | |
| # ] | |
| # DEFAULT_CONFIG_NAME = ( | |
| # "default" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| # ) | |
| # def _info(self): | |
| # features = datasets.Features( | |
| # { | |
| # "doc_text": datasets.Value("string"), | |
| # "summary_text": datasets.Value("string"), | |
| # "highlight_spans": 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 | |
| # # If there's a common (input, target) tuple from the features, | |
| # # specify them here. They'll be used if as_supervised=True in | |
| # # builder.as_dataset. | |
| # supervised_keys=None, | |
| # # Homepage of the dataset for documentation | |
| # homepage=_HOMEPAGE, | |
| # # License for the dataset if available | |
| # license=_LICENSE, | |
| # # Citation for the dataset | |
| # citation=_CITATION, | |
| # ) | |
| # def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager): | |
| # """Returns SplitGenerators.""" | |
| # URLs = _URLs[self.config.data_source] | |
| # # Download and prepare all files - keep same structure as URLs | |
| # corpora = {section: Path(dl_manager.download_and_extract(URLs[section])) | |
| # for section in URLs} | |
| # if self.config.data_source=="CNN-DM": | |
| # return [ | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TRAIN, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": corpora["train"] | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.VALIDATION, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": corpora["dev"] | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TEST, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": corpora["test"] | |
| # }, | |
| # ), | |
| # ] | |
| # else: | |
| # return [ | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TRAIN, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": corpora["train"] | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.VALIDATION, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": corpora["dev"] | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TEST, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": corpora["test"] | |
| # }, | |
| # ), | |
| # ] | |
| # def _generate_examples(self, filepath: List[str]): | |
| # """ Yields Controlled Text Reduction examples from a csv file. Each instance contains the document, the summary and the pre-selected spans.""" | |
| # # merge annotations from sections | |
| # df = pd.read_csv(filepath, index_col=False) | |
| # for counter, dic in enumerate(df.to_dict('records')): | |
| # columns_to_load_into_object = ["doc_text", "summary_text", "highlight_spans"] | |
| # for key in columns_to_load_into_object: | |
| # dic[key] = eval(dic[key]) | |
| # yield counter, dic | |
| ################################################################################################################################################# | |
| # 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. | |
| """A Dataset loading script for the QA-Discourse dataset (Pyatkin et. al., ACL 2020).""" | |
| import datasets | |
| from pathlib import Path | |
| from typing import List | |
| import pandas as pd | |
| _CITATION = """\ | |
| @inproceedings{pyatkin2020qadiscourse, | |
| title={QADiscourse-Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines}, | |
| author={Pyatkin, Valentina and Klein, Ayal and Tsarfaty, Reut and Dagan, Ido}, | |
| booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, | |
| pages={2804--2819}, | |
| year={2020} | |
| }""" | |
| _DESCRIPTION = """\ | |
| The dataset contains question-answer pairs to model discourse relations. | |
| While answers roughly correspond to spans of the sentence, these spans could have been freely adjusted by annotators to grammaticaly fit the question; | |
| Therefore, answers are given just as text and not as identified spans of the original sentence. | |
| See the paper for details: QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines, Pyatkin et. al., 2020 | |
| """ | |
| _HOMEPAGE = "https://github.com/ValentinaPy/QADiscourse" | |
| _LICENSE = """Resources on this page are licensed CC-BY 4.0, a Creative Commons license requiring Attribution (https://creativecommons.org/licenses/by/4.0/).""" | |
| _URLs = { | |
| "wikinews.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_train.tsv", | |
| "wikinews.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_dev.tsv", | |
| "wikinews.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_test.tsv", | |
| "wikipedia.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_train.tsv", | |
| "wikipedia.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_dev.tsv", | |
| "wikipedia.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_test.tsv", | |
| } | |
| COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer', | |
| 'question_start', 'question_aux', 'question_body', 'answer', | |
| 'untokenized sentence', 'target indices for untok sent'] | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class QaDiscourse(datasets.GeneratorBasedBuilder): | |
| """QA-Discourse: Discourse Relations as Question-Answer Pairs. """ | |
| VERSION = datasets.Version("1.0.2") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="plain_text", version=VERSION, description="This provides the QA-Discourse dataset" | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = ( | |
| "plain_text" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| ) | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "sentence": datasets.Value("string"), | |
| "sent_id": datasets.Value("string"), | |
| "question": datasets.Sequence(datasets.Value("string")), | |
| "answers": datasets.Sequence(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 | |
| # If there's a common (input, target) tuple from the features, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager): | |
| """Returns SplitGenerators.""" | |
| # Download and prepare all files - keep same structure as _URLs | |
| corpora = {section: Path(dl_manager.download_and_extract(_URLs[section])) | |
| for section in _URLs} | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepaths": [corpora["wikinews.train"], | |
| corpora["wikipedia.train"]], | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepaths": [corpora["wikinews.dev"], | |
| corpora["wikipedia.dev"]], | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepaths": [corpora["wikinews.test"], | |
| corpora["wikipedia.test"]], | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepaths: List[str]): | |
| """ | |
| Yields QA-Discourse examples from a tsv file. | |
| Sentences with no QAs will yield an ``empty QA'' record, where both 'question' and 'answers' are empty lists. | |
| """ | |
| # merge annotations from sections | |
| df = pd.concat([pd.read_csv(fn, sep='\t', error_bad_lines=False) for fn in filepaths]).reset_index(drop=True) | |
| df = df.applymap(str) # must turn all values to strings explicitly to avoid type errors | |
| for counter, row in df.iterrows(): | |
| # Prepare question (3 "slots" and question mark) | |
| question = [row.question_start, row.question_aux, row.question_body.rstrip('?'), '?'] | |
| answer = [row.answer] | |
| if row.question_start == "_": # sentence has no QAs | |
| question = [] | |
| answer = [] | |
| yield counter, { | |
| "sentence": row.sentence, | |
| "sent_id": row.qasrl_id, | |
| "question": question, | |
| "answers": answer, | |
| } | |