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import json |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_DATASETNAME = "iapp_squad" |
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_CITATION = """\ |
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@dataset |
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{ |
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kobkrit_viriyayudhakorn_2021_4539916, |
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author = {Kobkrit Viriyayudhakorn and Charin Polpanumas}, |
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title = {iapp_wiki_qa_squad}, |
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month = feb, |
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year = 2021, |
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publisher = {Zenodo}, |
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version = 1, |
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doi = {10.5281/zenodo.4539916}, |
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url = {https://doi.org/10.5281/zenodo.4539916} |
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} |
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""" |
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_DESCRIPTION = """ |
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`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. |
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It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) |
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to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in |
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5761/742/739 questions from 1529/191/192 articles. |
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""" |
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_HOMEPAGE = "https://github.com/iapp-technology/iapp-wiki-qa-dataset" |
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_LICENSE = Licenses.MIT.value |
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_HF_URL = " https://huggingface.co/datasets/iapp_wiki_qa_squad" |
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_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] |
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_LOCAL = False |
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_LANGUAGES = ["tha"] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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_URLS = { |
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"train": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/train.jsonl", |
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"validation": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/valid.jsonl", |
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"test": "https://raw.githubusercontent.com/iapp-technology/iapp-wiki-qa-dataset/main/squad_format/data/test.jsonl", |
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} |
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class IappWikiQASquadDataset(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig(name=f"{_DATASETNAME}_source", version=datasets.Version(_SOURCE_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="source"), |
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SEACrowdConfig(name=f"{_DATASETNAME}_seacrowd_qa", version=datasets.Version(_SEACROWD_VERSION), description=_DESCRIPTION, subset_id=f"{_DATASETNAME}", schema="seacrowd_qa"), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"question_id": datasets.Value("string"), |
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"article_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answers": datasets.features.Sequence( |
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{ |
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"text": datasets.Value("string"), |
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"answer_start": datasets.Value("int32"), |
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"answer_end": datasets.Value("int32"), |
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} |
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), |
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} |
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) |
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elif self.config.schema == "seacrowd_qa": |
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features = schemas.qa_features |
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features["meta"] = { |
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"answer_start": datasets.Value("int32"), |
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"answer_end": datasets.Value("int32"), |
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} |
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return datasets.DatasetInfo(description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, license=_LICENSE) |
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def _split_generators(self, dl_manager): |
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file_paths = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": file_paths["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": file_paths["validation"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": file_paths["test"]}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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data = json.loads(row) |
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if self.config.schema == "source": |
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yield id_, { |
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"question_id": data["question_id"], |
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"article_id": data["article_id"], |
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"title": data["title"], |
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"context": data["context"], |
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"question": data["question"], |
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"answers": { |
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"text": data["answers"]["text"], |
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"answer_start": data["answers"]["answer_start"], |
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"answer_end": data["answers"]["answer_end"], |
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}, |
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} |
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elif self.config.schema == "seacrowd_qa": |
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yield id_, { |
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"id": id_, |
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"question_id": data["question_id"], |
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"document_id": data["article_id"], |
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"question": data["question"], |
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"type": "abstractive", |
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"choices": [], |
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"context": data["context"], |
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"answer": data["answers"]["text"], |
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"meta": {"answer_start": data["answers"]["answer_start"][0], "answer_end": data["answers"]["answer_end"][0]}, |
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} |
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