Datasets:
subjqa

	# 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	# limitations under the License.
	"""SubjQA is a question answering dataset that focuses on subjective questions and answers.
	The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
	electronics, TripAdvisor (i.e. hotels), and restaurants."""


	import ast
	import os

	import pandas as pd

	import datasets


	_CITATION = """\
	@inproceedings{bjerva20subjqa,
	title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
	author = "Bjerva, Johannes and
	Bhutani, Nikita and
	Golahn, Behzad and
	Tan, Wang-Chiew and
	Augenstein, Isabelle",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
	month = November,
	year = "2020",
	publisher = "Association for Computational Linguistics",
	}
	"""

	_DESCRIPTION = """SubjQA is a question answering dataset that focuses on subjective questions and answers.
	The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
	electronics, TripAdvisor (i.e. hotels), and restaurants."""

	_HOMEPAGE = ""

	_LICENSE = ""

	# From: https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip
	_URLs = {"default": "data.zip"}


	class Subjqa(datasets.GeneratorBasedBuilder):
	"""SubjQA is a question answering dataset that focuses on subjective questions and answers."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="books", version=VERSION, description="Amazon book reviews"),
	datasets.BuilderConfig(name="electronics", version=VERSION, description="Amazon electronics reviews"),
	datasets.BuilderConfig(name="grocery", version=VERSION, description="Amazon grocery reviews"),
	datasets.BuilderConfig(name="movies", version=VERSION, description="Amazon movie reviews"),
	datasets.BuilderConfig(name="restaurants", version=VERSION, description="Yelp restaurant reviews"),
	datasets.BuilderConfig(name="tripadvisor", version=VERSION, description="TripAdvisor hotel reviews"),
	]

	def _info(self):
	features = datasets.Features(
	{
	"domain": datasets.Value("string"),
	"nn_mod": datasets.Value("string"),
	"nn_asp": datasets.Value("string"),
	"query_mod": datasets.Value("string"),
	"query_asp": datasets.Value("string"),
	"q_reviews_id": datasets.Value("string"),
	"question_subj_level": datasets.Value("int64"),
	"ques_subj_score": datasets.Value("float"),
	"is_ques_subjective": datasets.Value("bool"),
	"review_id": datasets.Value("string"),
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	"answer_subj_level": datasets.Value("int64"),
	"ans_subj_score": datasets.Value("float"),
	"is_ans_subjective": datasets.Value("bool"),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URLs["default"])
	data_dir = os.path.join(data_dir, "SubjQA-master", "SubjQA", self.config.name, "splits")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.csv")
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test.csv")
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "dev.csv")
	},
	),
	]

	def _generate_examples(self, filepath):
	df = pd.read_csv(filepath)
	squad_format = self._convert_to_squad(df)
	for example in squad_format["data"]:
	title = example.get("title", "").strip()
	for paragraph in example["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	question_meta = {k: v for k, v in qa.items() if k in self.question_meta_columns}
	id_ = qa["id"]
	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"].strip() for answer in qa["answers"]]
	answer_meta = pd.DataFrame(qa["answers"], columns=self.answer_meta_columns).to_dict("list")
	yield id_, {
	**{
	"title": title,
	"context": context,
	"question": question,
	"id": id_,
	"answers": {
	**{
	"answer_start": answer_starts,
	"text": answers,
	},
	**answer_meta,
	},
	},
	**question_meta,
	}

	def _create_paragraphs(self, df):
	"A helper function to convert a pandas.DataFrame of (question, context, answer) rows to SQuAD paragraphs."
	self.question_meta_columns = [
	"domain",
	"nn_mod",
	"nn_asp",
	"query_mod",
	"query_asp",
	"q_reviews_id",
	"question_subj_level",
	"ques_subj_score",
	"is_ques_subjective",
	"review_id",
	]
	self.answer_meta_columns = ["answer_subj_level", "ans_subj_score", "is_ans_subjective"]
	id2review = dict(zip(df["review_id"], df["review"]))
	pars = []
	for review_id, review in id2review.items():
	qas = []
	review_df = df.query(f"review_id == '{review_id}'")
	id2question = dict(zip(review_df["q_review_id"], review_df["question"]))

	for k, v in id2question.items():
	d = df.query(f"q_review_id == '{k}'").to_dict(orient="list")
	answer_starts = [ast.literal_eval(a)[0] for a in d["human_ans_indices"]]
	answer_meta = {k: v[0] for k, v in d.items() if k in self.answer_meta_columns}
	question_meta = {k: v[0] for k, v in d.items() if k in self.question_meta_columns}
	# Only fill answerable questions
	if pd.unique(d["human_ans_spans"])[0] != "ANSWERNOTFOUND":
	answers = [
	{{"text": text, "answer_start": answer_start}, answer_meta}
	for text, answer_start in zip(d["human_ans_spans"], answer_starts)
	if text != "ANSWERNOTFOUND"
	]
	else:
	answers = []
	qas.append({{"question": v, "id": k, "answers": answers}, question_meta})
	# Slice off ANSWERNOTFOUND from context
	pars.append({"qas": qas, "context": review[: -len(" ANSWERNOTFOUND")]})
	return pars

	def _convert_to_squad(self, df):
	"A helper function to convert a pandas.DataFrame of product-based QA dataset into SQuAD format"
	groups = (
	df.groupby("item_id")
	.apply(self._create_paragraphs)
	.to_frame(name="paragraphs")
	.reset_index()
	.rename(columns={"item_id": "title"})
	)
	squad_data = {}
	squad_data["data"] = groups.to_dict(orient="records")
	return squad_data