# 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. """CoQA dataset. This `CoQA` adds the "additional_answers" feature that's missing in the original datasets version: https://github.com/huggingface/datasets/blob/master/datasets/coqa/coqa.py """ import json import datasets _CITATION = """\ @misc{reddy2018coqa, title={CoQA: A Conversational Question Answering Challenge}, author={Siva Reddy and Danqi Chen and Christopher D. Manning}, year={2018}, eprint={1808.07042}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DESCRIPTION = """\ CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. """ _HOMEPAGE = "https://stanfordnlp.github.io/coqa/" _LICENSE = "Different licenses depending on the content (see https://stanfordnlp.github.io/coqa/ for details)" _URLS = { "train": "https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json", "validation": "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json", } # `additional_answers` are not available in the train set so we fill them with # empty dicts of the same form. _EMPTY_ADDITIONAL_ANSWER = { "0": [ { "span_start": -1, "span_end": -1, "span_text": "", "input_text": "", "turn_id": -1, } ], "1": [ { "span_start": -1, "span_end": -1, "span_text": "", "input_text": "", "turn_id": -1, } ], "2": [ { "span_start": -1, "span_end": -1, "span_text": "", "input_text": "", "turn_id": -1, } ], } class Coqa(datasets.GeneratorBasedBuilder): """CoQA is a large-scale dataset for building Conversational Question Answering systems.""" VERSION = datasets.Version("0.0.1") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="coqa", version=VERSION, description="The CoQA dataset." ), ] def _info(self): features = datasets.Features( { "id": datasets.Value("string"), "source": datasets.Value("string"), "story": datasets.Value("string"), "questions": datasets.features.Sequence( { "input_text": datasets.Value("string"), "turn_id": datasets.Value("int32"), } ), "answers": datasets.features.Sequence( { "span_start": datasets.Value("int32"), "span_end": datasets.Value("int32"), "span_text": datasets.Value("string"), "input_text": datasets.Value("string"), "turn_id": datasets.Value("int32"), } ), "additional_answers": { "0": datasets.features.Sequence( { "span_start": datasets.Value("int32"), "span_end": datasets.Value("int32"), "span_text": datasets.Value("string"), "input_text": datasets.Value("string"), "turn_id": datasets.Value("int32"), } ), "1": datasets.features.Sequence( { "span_start": datasets.Value("int32"), "span_end": datasets.Value("int32"), "span_text": datasets.Value("string"), "input_text": datasets.Value("string"), "turn_id": datasets.Value("int32"), } ), "2": datasets.features.Sequence( { "span_start": datasets.Value("int32"), "span_end": datasets.Value("int32"), "span_text": datasets.Value("string"), "input_text": datasets.Value("string"), "turn_id": datasets.Value("int32"), } ), }, } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): urls = {"train": _URLS["train"], "validation": _URLS["validation"]} data_dirs = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dirs["train"], "split": datasets.Split.TRAIN, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dirs["validation"], "split": datasets.Split.VALIDATION, }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): with open(filepath, encoding="utf-8") as f: data = json.load(f) for row in data["data"]: id = row["id"] source = row["source"] story = row["story"] questions = [ {"input_text": q["input_text"], "turn_id": q["turn_id"]} for q in row["questions"] ] answers = [ { "span_start": a["span_start"], "span_end": a["span_end"], "span_text": a["span_text"], "input_text": a["input_text"], "turn_id": a["turn_id"], } for a in row["answers"] ] if split == datasets.Split.TRAIN: additional_answers = _EMPTY_ADDITIONAL_ANSWER else: additional_answers = { "0": [ { "span_start": a0["span_start"], "span_end": a0["span_end"], "span_text": a0["span_text"], "input_text": a0["input_text"], "turn_id": a0["turn_id"], } for a0 in row["additional_answers"]["0"] ], "1": [ { "span_start": a1["span_start"], "span_end": a1["span_end"], "span_text": a1["span_text"], "input_text": a1["input_text"], "turn_id": a1["turn_id"], } for a1 in row["additional_answers"]["1"] ], "2": [ { "span_start": a2["span_start"], "span_end": a2["span_end"], "span_text": a2["span_text"], "input_text": a2["input_text"], "turn_id": a2["turn_id"], } for a2 in row["additional_answers"]["2"] ], } yield row["id"], { "id": id, "story": story, "source": source, "questions": questions, "answers": answers, "additional_answers": additional_answers, }