Datasets:
Languages:
French
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
| """FQuAD dataset.""" | |
| import json | |
| import os | |
| from textwrap import dedent | |
| import datasets | |
| _HOMEPAGE = "https://fquad.illuin.tech/" | |
| _DESCRIPTION = """\ | |
| FQuAD: French Question Answering Dataset | |
| We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs. | |
| Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%. | |
| """ | |
| _CITATION = """\ | |
| @ARTICLE{2020arXiv200206071 | |
| author = {Martin, d'Hoffschmidt and Maxime, Vidal and | |
| Wacim, Belblidia and Tom, Brendlé}, | |
| title = "{FQuAD: French Question Answering Dataset}", | |
| journal = {arXiv e-prints}, | |
| keywords = {Computer Science - Computation and Language}, | |
| year = "2020", | |
| month = "Feb", | |
| eid = {arXiv:2002.06071}, | |
| pages = {arXiv:2002.06071}, | |
| archivePrefix = {arXiv}, | |
| eprint = {2002.06071}, | |
| primaryClass = {cs.CL} | |
| } | |
| """ | |
| class Fquad(datasets.GeneratorBasedBuilder): | |
| """FQuAD dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| def manual_download_instructions(self): | |
| return dedent("""\ | |
| To access the data for this dataset, you need to request it at: | |
| https://fquad.illuin.tech/#download | |
| Unzip the downloaded file with `unzip download-form-fquad1.0.zip -d <path/to/directory>`, into a destination | |
| directory <path/to/directory>, which will contain the two data files train.json and valid.json. | |
| To load the dataset, pass the full path to the destination directory | |
| in your call to the loading function: `datasets.load_dataset("fquad", data_dir="<path/to/directory>")` | |
| """) | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "context": datasets.Value("string"), | |
| "questions": datasets.features.Sequence(datasets.Value("string")), | |
| "answers": datasets.features.Sequence( | |
| {"texts": datasets.Value("string"), "answers_starts": datasets.Value("int32")} | |
| ), | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ), | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": os.path.join(data_dir, "train.json")}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": os.path.join(data_dir, "valid.json")}, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """Yields examples.""" | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for id1, examples in enumerate(data["data"]): | |
| for id2, example in enumerate(examples["paragraphs"]): | |
| questions = [question["question"] for question in example["qas"]] | |
| answers = [answer["answers"] for answer in example["qas"]] | |
| texts = [answer[0]["text"] for answer in answers] | |
| answers_starts = [answer[0]["answer_start"] for answer in answers] | |
| yield str(id1) + "_" + str(id2), { | |
| "context": example["context"], | |
| "questions": questions, | |
| "answers": {"texts": texts, "answers_starts": answers_starts}, | |
| } | |