Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
ArXiv:
License:
| """TODO(coqa): Add a description here.""" | |
| import json | |
| import datasets | |
| # TODO(coqa): BibTeX citation | |
| _CITATION = """\ | |
| @InProceedings{SivaAndAl:Coca, | |
| author = {Siva, Reddy and Danqi, Chen and Christopher D., Manning}, | |
| title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering}, | |
| journal = { arXiv}, | |
| year = {2018}, | |
| } | |
| """ | |
| # TODO(coqa): | |
| _DESCRIPTION = """\ | |
| CoQA: A Conversational Question Answering Challenge | |
| """ | |
| _TRAIN_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json" | |
| _DEV_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json" | |
| class Coqa(datasets.GeneratorBasedBuilder): | |
| """TODO(coqa): Short description of my dataset.""" | |
| # TODO(coqa): Set up version. | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| # TODO(coqa): Specifies the datasets.DatasetInfo object | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # datasets.features.FeatureConnectors | |
| features=datasets.Features( | |
| { | |
| "source": datasets.Value("string"), | |
| "story": datasets.Value("string"), | |
| "questions": datasets.features.Sequence(datasets.Value("string")), | |
| "answers": datasets.features.Sequence( | |
| { | |
| "input_text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| "answer_end": datasets.Value("int32"), | |
| } | |
| ), | |
| } | |
| ), | |
| # If there's a common (input, target) tuple from the features, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # Homepage of the dataset for documentation | |
| homepage="https://stanfordnlp.github.io/coqa/", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| # TODO(coqa): Downloads the data and defines the splits | |
| # dl_manager is a datasets.download.DownloadManager that can be used to | |
| # download and extract URLs | |
| urls_to_download = {"train": _TRAIN_DATA_URL, "dev": _DEV_DATA_URL} | |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"} | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| """Yields examples.""" | |
| # TODO(coqa): Yields (key, example) tuples from the dataset | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for row in data["data"]: | |
| questions = [question["input_text"] for question in row["questions"]] | |
| story = row["story"] | |
| source = row["source"] | |
| answers_start = [answer["span_start"] for answer in row["answers"]] | |
| answers_end = [answer["span_end"] for answer in row["answers"]] | |
| answers = [answer["input_text"] for answer in row["answers"]] | |
| yield row["id"], { | |
| "source": source, | |
| "story": story, | |
| "questions": questions, | |
| "answers": {"input_text": answers, "answer_start": answers_start, "answer_end": answers_end}, | |
| } | |