# coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # 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. # Lint as: python3 """Quora question pairs data""" import csv import datasets _DESCRIPTION = "The Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning)." _URL = "http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv" class Quora(datasets.GeneratorBasedBuilder): """Quora Question Pairs dataset""" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "questions": datasets.features.Sequence( { "id": datasets.Value("int32"), "text": datasets.Value("string"), } ), "is_duplicate": datasets.Value("bool"), } ), homepage="https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs", ) def _split_generators(self, dl_manager): data_file = dl_manager.download_and_extract({"data_file": _URL}) return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=data_file)] def _generate_examples(self, data_file): with open(data_file, encoding="utf-8") as f: data = csv.DictReader(f, delimiter="\t") for idx, row in enumerate(data): yield idx, { "questions": [ {"id": row["qid1"], "text": row["question1"]}, {"id": row["qid2"], "text": row["question2"]}, ], "is_duplicate": row["is_duplicate"] == "1", }