import json from typing import Dict, List, Optional, Union import datasets as ds import pandas as pd _CITATION = """ @InProceedings{Kurihara_nlp2020, 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", title = "JAQKET: クイズを題材にした日本語 QA データセットの構築", booktitle = "言語処理学会第26回年次大会", year = "2020", url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf" note= "in Japanese" """ _DESCRIPTION = """\ JAQKET: JApanese Questions on Knowledge of EnTitie """ _HOMEPAGE = "https://sites.google.com/view/project-aio/dataset" _LICENSE = """\ This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. """ _DESCRIPTION_CONFIGS = { "v1.0": "v1.0", "v2.0": "v2.0", } _URLS = { "v1.0": { "train": "https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_01/train_questions.json", "valid": "https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_01/dev1_questions.json", "candidate_entities": "https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_01/candidate_entities.json.gz", }, "v2.0": { "train": "https://huggingface.co/datasets/kumapo/JAQKET/resolve/main/train_jaqket_59.350.json", "valid": "https://huggingface.co/datasets/kumapo/JAQKET/resolve/main/dev_jaqket_59.350.json", }, } def dataset_info_v1() -> ds.Features: features = ds.Features( { "qid": ds.Value("string"), "question": ds.Value("string"), "answer_entity": ds.Value("string"), "label": ds.Value("int32"), "answer_candidates": ds.Sequence( ds.Value("string"), ), "contexts": ds.Sequence( ds.Value("string") ) } ) return ds.DatasetInfo( description=_DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, license=_LICENSE, features=features, ) def dataset_info_v2() -> ds.Features: features = ds.Features( { "qid": ds.Value("string"), "question": ds.Value("string"), "answers": ds.Sequence({ "text": ds.Value("string"), "answer_start": ds.Value("int32"), }), "ctxs": ds.Sequence({ "id": ds.Value("string"), "title": ds.Value("string"), "text": ds.Value("string"), "score": ds.Value("float32"), "has_answer": ds.Value("bool"), }) } ) return ds.DatasetInfo( description=_DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, license=_LICENSE, features=features, ) class JAQKETBuilderConfig(ds.BuilderConfig): def __init__(self, name, num_contexts, **kwargs): super().__init__(name, **kwargs) self.num_contexts = num_contexts class JAQKET(ds.GeneratorBasedBuilder): VERSION = ds.Version("0.3.0") BUILDER_CONFIGS = [ JAQKETBuilderConfig( name="v1.0", version=VERSION, description=_DESCRIPTION_CONFIGS["v1.0"], num_contexts=5, ), JAQKETBuilderConfig( name="v2.0", version=VERSION, description=_DESCRIPTION_CONFIGS["v2.0"], num_contexts=5, ), ] def _info(self) -> ds.DatasetInfo: if self.config.name == "v1.0": return dataset_info_v1() elif self.config.name == "v2.0": return dataset_info_v2() else: raise ValueError(f"Invalid config name: {self.config.name}") def _split_generators(self, dl_manager: ds.DownloadManager): file_paths = dl_manager.download_and_extract(_URLS[self.config.name]) if self.config.name == "v1.0": return [ ds.SplitGenerator( name=ds.Split.TRAIN, gen_kwargs={"file_path": file_paths["train"], "entities_file_path": file_paths["candidate_entities"]}, ), ds.SplitGenerator( name=ds.Split.VALIDATION, gen_kwargs={"file_path": file_paths["valid"], "entities_file_path": file_paths["candidate_entities"]}, ), ] elif self.config.name == "v2.0": return [ ds.SplitGenerator( name=ds.Split.TRAIN, gen_kwargs={"file_path": file_paths["train"]}, ), ds.SplitGenerator( name=ds.Split.VALIDATION, gen_kwargs={"file_path": file_paths["valid"]}, ), ] else: raise ValueError(f"Invalid config name: {self.config.name}") def _generate_examples_v1( self, file_path: str, entities_file_path: str, num_contexts: int, ): if file_path is None or entities_file_path is None: raise ValueError(f"Invalid argument for {self.config.name}") if num_contexts is None: num_contexts = 20 # maximum entities = dict() with open(entities_file_path, "r", encoding="utf-8") as fin: lines = fin.readlines() for line in lines: entity = json.loads(line.strip()) entities[entity["title"]] = entity["text"] with open(file_path, "r", encoding="utf-8") as fin: lines = fin.readlines() for line in lines: data_raw = json.loads(line.strip("\n")) q_id = data_raw["qid"] question = data_raw["question"].replace("_", "") answer_entity = data_raw["answer_entity"] answer_candidates = data_raw["answer_candidates"][:num_contexts] if answer_entity not in answer_candidates: continue if len(answer_candidates) != num_contexts: continue contexts = [entities[answer_candidates[i]] for i in range(num_contexts)] label = str(answer_candidates.index(answer_entity)) example_dict = { "qid": q_id, "question": question, "answer_entity": answer_entity, "label": label, "answer_candidates": answer_candidates, "contexts": contexts, } yield q_id, example_dict def _generate_examples_v2( self, file_path: str, num_contexts: int, ): if file_path is None: raise ValueError(f"Invalid argument for {self.config.name}") if num_contexts is None: num_contexts = 100 # it's the largest in acc@k on https://github.com/cl-tohoku/AIO2_DPR_baseline with open(file_path, "r") as rf: json_data = json.load(rf) for json_dict in json_data: q_id = json_dict["qid"] question = json_dict["question"] answers = [ {"text": answer, "answer_start": -1 } # -1: dummy for answer in json_dict["answers"] ] has_answer = [ctx["has_answer"] for ctx in json_dict["ctxs"][:num_contexts]] if True not in has_answer: continue ctxs = [ { "id": ctx["id"], "title": ctx["title"], "text": ctx["text"], "score": float(ctx["score"]), "has_answer": ctx["has_answer"] } for ctx in json_dict["ctxs"][:num_contexts] ] example_dict = { "qid": q_id, "question": question, "answers": answers, "ctxs": ctxs } yield q_id, example_dict def _generate_examples( self, file_path: str, entities_file_path: Optional[str] = None, ): if self.config.name == "v1.0": yield from self._generate_examples_v1(file_path, entities_file_path, self.config.num_contexts) elif self.config.name == "v2.0": yield from self._generate_examples_v2(file_path, self.config.num_contexts) else: raise ValueError(f"Invalid config name: {self.config.name}")