# # 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, }