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import json |
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from typing import Dict, List, Optional, Union |
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import datasets as ds |
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import pandas as pd |
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_CITATION = """ |
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@InProceedings{Kurihara_nlp2020, |
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author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也", |
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title = "JAQKET: クイズを題材にした日本語 QA データセットの構築", |
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booktitle = "言語処理学会第26回年次大会", |
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year = "2020", |
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url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf" |
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note= "in Japanese" |
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""" |
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_DESCRIPTION = """\ |
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JAQKET: JApanese Questions on Knowledge of EnTitie |
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""" |
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_HOMEPAGE = "https://sites.google.com/view/project-aio/dataset" |
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_LICENSE = """\ |
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |
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""" |
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_DESCRIPTION_CONFIGS = { |
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"v1.0": "v1.0", |
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"v2.0": "v2.0", |
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} |
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_URLS = { |
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"v1.0": { |
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"train": "https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_01/train_questions.json", |
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"valid": "https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_01/dev1_questions.json", |
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"candidate_entities": "https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_01/candidate_entities.json.gz", |
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}, |
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"v2.0": { |
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"train": "https://huggingface.co/datasets/kumapo/JAQKET/resolve/main/train_jaqket_59.350.json", |
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"valid": "https://huggingface.co/datasets/kumapo/JAQKET/resolve/main/dev_jaqket_59.350.json", |
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}, |
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} |
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def dataset_info_v1() -> ds.Features: |
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features = ds.Features( |
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{ |
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"qid": ds.Value("string"), |
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"question": ds.Value("string"), |
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"answer_entity": ds.Value("string"), |
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"label": ds.Value("int32"), |
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"answer_candidates": ds.Sequence( |
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ds.Value("string"), |
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), |
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"contexts": ds.Sequence( |
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ds.Value("string") |
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) |
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} |
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) |
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return ds.DatasetInfo( |
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description=_DESCRIPTION, |
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citation=_CITATION, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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features=features, |
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) |
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def dataset_info_v2() -> ds.Features: |
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features = ds.Features( |
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{ |
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"qid": ds.Value("string"), |
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"question": ds.Value("string"), |
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"answers": ds.Sequence({ |
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"text": ds.Value("string"), |
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"answer_start": ds.Value("int32"), |
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}), |
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"ctxs": ds.Sequence({ |
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"id": ds.Value("string"), |
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"title": ds.Value("string"), |
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"text": ds.Value("string"), |
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"score": ds.Value("float32"), |
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"has_answer": ds.Value("bool"), |
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}) |
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} |
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) |
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return ds.DatasetInfo( |
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description=_DESCRIPTION, |
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citation=_CITATION, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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features=features, |
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) |
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class JAQKETBuilderConfig(ds.BuilderConfig): |
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def __init__(self, name, num_contexts, **kwargs): |
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super().__init__(name, **kwargs) |
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self.num_contexts = num_contexts |
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class JAQKET(ds.GeneratorBasedBuilder): |
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VERSION = ds.Version("0.3.0") |
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BUILDER_CONFIGS = [ |
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JAQKETBuilderConfig( |
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name="v1.0", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["v1.0"], |
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num_contexts=5, |
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), |
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JAQKETBuilderConfig( |
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name="v2.0", |
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version=VERSION, |
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description=_DESCRIPTION_CONFIGS["v2.0"], |
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num_contexts=5, |
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), |
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] |
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def _info(self) -> ds.DatasetInfo: |
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if self.config.name == "v1.0": |
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return dataset_info_v1() |
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elif self.config.name == "v2.0": |
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return dataset_info_v2() |
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else: |
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raise ValueError(f"Invalid config name: {self.config.name}") |
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def _split_generators(self, dl_manager: ds.DownloadManager): |
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file_paths = dl_manager.download_and_extract(_URLS[self.config.name]) |
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if self.config.name == "v1.0": |
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return [ |
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ds.SplitGenerator( |
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name=ds.Split.TRAIN, |
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gen_kwargs={"file_path": file_paths["train"], "entities_file_path": file_paths["candidate_entities"]}, |
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), |
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ds.SplitGenerator( |
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name=ds.Split.VALIDATION, |
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gen_kwargs={"file_path": file_paths["valid"], "entities_file_path": file_paths["candidate_entities"]}, |
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), |
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] |
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elif self.config.name == "v2.0": |
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return [ |
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ds.SplitGenerator( |
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name=ds.Split.TRAIN, |
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gen_kwargs={"file_path": file_paths["train"]}, |
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), |
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ds.SplitGenerator( |
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name=ds.Split.VALIDATION, |
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gen_kwargs={"file_path": file_paths["valid"]}, |
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), |
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] |
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else: |
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raise ValueError(f"Invalid config name: {self.config.name}") |
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def _generate_examples_v1( |
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self, |
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file_path: str, |
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entities_file_path: str, |
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num_contexts: int, |
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): |
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if file_path is None or entities_file_path is None: |
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raise ValueError(f"Invalid argument for {self.config.name}") |
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if num_contexts is None: |
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num_contexts = 20 |
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entities = dict() |
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with open(entities_file_path, "r", encoding="utf-8") as fin: |
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lines = fin.readlines() |
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for line in lines: |
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entity = json.loads(line.strip()) |
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entities[entity["title"]] = entity["text"] |
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with open(file_path, "r", encoding="utf-8") as fin: |
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lines = fin.readlines() |
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for line in lines: |
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data_raw = json.loads(line.strip("\n")) |
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q_id = data_raw["qid"] |
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question = data_raw["question"].replace("_", "") |
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answer_entity = data_raw["answer_entity"] |
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answer_candidates = data_raw["answer_candidates"][:num_contexts] |
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if answer_entity not in answer_candidates: |
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continue |
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if len(answer_candidates) != num_contexts: |
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continue |
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contexts = [entities[answer_candidates[i]] for i in range(num_contexts)] |
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label = str(answer_candidates.index(answer_entity)) |
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example_dict = { |
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"qid": q_id, |
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"question": question, |
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"answer_entity": answer_entity, |
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"label": label, |
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"answer_candidates": answer_candidates, |
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"contexts": contexts, |
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} |
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yield q_id, example_dict |
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def _generate_examples_v2( |
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self, |
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file_path: str, |
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num_contexts: int, |
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): |
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if file_path is None: |
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raise ValueError(f"Invalid argument for {self.config.name}") |
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if num_contexts is None: |
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num_contexts = 100 |
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with open(file_path, "r") as rf: |
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json_data = json.load(rf) |
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for json_dict in json_data: |
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q_id = json_dict["qid"] |
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question = json_dict["question"] |
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answers = [ |
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{"text": answer, "answer_start": -1 } |
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for answer in json_dict["answers"] |
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] |
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has_answer = [ctx["has_answer"] for ctx in json_dict["ctxs"][:num_contexts]] |
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if True not in has_answer: |
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continue |
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ctxs = [ |
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{ |
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"id": ctx["id"], |
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"title": ctx["title"], |
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"text": ctx["text"], |
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"score": float(ctx["score"]), |
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"has_answer": ctx["has_answer"] |
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} |
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for ctx in json_dict["ctxs"][:num_contexts] |
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] |
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example_dict = { |
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"qid": q_id, |
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"question": question, |
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"answers": answers, |
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"ctxs": ctxs |
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} |
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yield q_id, example_dict |
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def _generate_examples( |
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self, |
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file_path: str, |
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entities_file_path: Optional[str] = None, |
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): |
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if self.config.name == "v1.0": |
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yield from self._generate_examples_v1(file_path, entities_file_path, self.config.num_contexts) |
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elif self.config.name == "v2.0": |
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yield from self._generate_examples_v2(file_path, self.config.num_contexts) |
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else: |
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raise ValueError(f"Invalid config name: {self.config.name}") |
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