# 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. """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