Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Size:
1K<n<10K
ArXiv:
License:
| # 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 = "" | |
| _URLs = {"default": "https://github.com/lewtun/SubjQA/archive/refs/heads/master.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"]) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, f"SubjQA-master/SubjQA/{self.config.name}/splits/train.csv") | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, f"SubjQA-master/SubjQA/{self.config.name}/splits/test.csv") | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, f"SubjQA-master/SubjQA/{self.config.name}/splits/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 | |