Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
Source Datasets:
extended|wikipedia
Tags:
License:
| # 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 | |
| """SQUAD: The Stanford Question Answering Dataset.""" | |
| import json | |
| import datasets | |
| from datasets.tasks import QuestionAnsweringExtractive | |
| logger = datasets.logging.get_logger(__name__) | |
| #_URL = "https://huggingface.co/datasets/Lexi/NQ_squad_format/blob/main/" | |
| _URLS = { | |
| "train": "train.json", | |
| "dev": "dev_incomplete.json", | |
| "test": "openbook_beam_5.json" | |
| } | |
| class SquadConfig(datasets.BuilderConfig): | |
| """BuilderConfig for SQUAD.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for SQUAD. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(SquadConfig, self).__init__(**kwargs) | |
| class Squad(datasets.GeneratorBasedBuilder): | |
| """SQUAD: The Stanford Question Answering Dataset. Version 1.1.""" | |
| BUILDER_CONFIGS = [ | |
| SquadConfig( | |
| name="plain_text", | |
| version=datasets.Version("1.0.0", ""), | |
| description="Plain text", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| #description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("int32"), | |
| "context": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answers": datasets.features.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| } | |
| ), | |
| } | |
| ), | |
| # No default supervised_keys (as we have to pass both question | |
| # and context as input). | |
| supervised_keys=None, | |
| task_templates=[ | |
| QuestionAnsweringExtractive( | |
| question_column="question", context_column="context", answers_column="answers" | |
| ) | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| key = 0 | |
| print(filepath) | |
| with open(filepath, 'rb') as f: | |
| data = json.load(f) | |
| print("example data: ", data[0]) | |
| print("number of data: ", len(data)) | |
| print("data keys: ", data[0].keys()) | |
| for line in data: | |
| yield key, { | |
| "context": line['context'], | |
| "question": line["question"], | |
| "id": line["id"], | |
| "answers": line['answers'] | |
| } | |
| key += 1 | |