Datasets:
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| import json | |
| import itertools | |
| import os | |
| import datasets | |
| from datasets import DatasetInfo | |
| _URL = "" | |
| _QUALITY_CITATION = """ | |
| @inproceedings{pang-etal-2022-quality, | |
| title = "{Q}u{ALITY}: Question Answering with Long Input Texts, Yes!", | |
| author = "Pang, Richard Yuanzhe and | |
| Parrish, Alicia and | |
| Joshi, Nitish and | |
| Nangia, Nikita and | |
| Phang, Jason and | |
| Chen, Angelica and | |
| Padmakumar, Vishakh and | |
| Ma, Johnny and | |
| Thompson, Jana and | |
| He, He and | |
| Bowman, Samuel", | |
| booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| month = jul, | |
| year = "2022", | |
| address = "Seattle, United States", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2022.naacl-main.391", | |
| pages = "5336--5358", | |
| abstract = "To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, our questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts. In addition, only half of the questions are answerable by annotators working under tight time constraints, indicating that skimming and simple search are not enough to consistently perform well. Our baseline models perform poorly on this task (55.4{\%}) and significantly lag behind human performance (93.5{\%}).", | |
| } | |
| """ | |
| class QualityConfig(datasets.BuilderConfig): | |
| def __init__(self, features, data_url, citation, label_classes=("0", "1", "2", "3"), **kwargs): | |
| super(QualityConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs) | |
| self.features = features | |
| self.data_url = data_url | |
| self.label_classes = label_classes | |
| self.citation = citation | |
| class Quality(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = list(itertools.chain.from_iterable([ | |
| [ | |
| QualityConfig( | |
| name=f"dpr-first-{x * 5}%-maxlen-{maxlen}", | |
| features=['question', "context", "output", "article_id", "difficulty"], | |
| data_url=f"extractive_dpr_agent_first_20splits_maxlen{maxlen}/agent_{x}.zip", | |
| citation=_QUALITY_CITATION, | |
| description=f"Using DPR (NYU-version) to summarize first {x * 5}% of the document within {maxlen} max tokens" | |
| ) for x in range(0, 20) | |
| ] for maxlen in [150, 300, 400, 500] | |
| ])) + list(itertools.chain.from_iterable([ | |
| [ | |
| QualityConfig( | |
| name=f"dpr-rest-{x * 5}%-maxlen-{maxlen}", | |
| features=['question', "context", "output", "article_id", "difficulty"], | |
| data_url=f"extractive_dpr_agent_rest_20splits_maxlen{maxlen}/agent_{x}.zip", | |
| citation=_QUALITY_CITATION, | |
| description=f"Using DPR (NYU-version) to summarize rest {x * 5}% of the document within {maxlen} max tokens" | |
| ) for x in range(0, 20) | |
| ] for maxlen in [25, 50, 100, 150, 300, 400, 500] | |
| ])) | |
| #+ [ | |
| #QualityConfig( | |
| #name=f"original", | |
| #features=['question', "context", "output"], | |
| #data_url=f"original/original.zip", | |
| #citation=_QUALITY_CITATION, | |
| #description=f"original QuALITY data" | |
| #), | |
| #] | |
| # + [ | |
| # QualityConfig( | |
| # name=f"dpr-rest-{x}%-maxlen-25", | |
| # features=['question', "context", "option"], | |
| # data_url=_URL + f"/extractive_dpr_agent_first_20splits_maxlen25/agent_{x}/data.zip", | |
| # citation=_QUALITY_CITATION, | |
| # description=f"Using DPR (NYU-version) to summarize rest {x}% of the document within 150 max tokens" | |
| # ) for x in range(0, 20) | |
| # ] | |
| def _info(self) -> DatasetInfo: | |
| features = {feature: datasets.Value("string") for feature in self.config.features} | |
| features['options'] = datasets.Sequence(datasets.Value("string")) | |
| return datasets.DatasetInfo( | |
| description=self.config.description, | |
| features=datasets.Features(features), | |
| citation=_QUALITY_CITATION | |
| ) | |
| def _split_generators(self, dl_manager): | |
| dl_dir = dl_manager.download_and_extract(self.config.data_url) or "" | |
| task_name = _get_task_name_from_data_url(self.config.data_url) | |
| dl_dir = os.path.join(dl_dir, task_name) | |
| #if self.config.name in ["axb", "axg"]: | |
| #return [ | |
| #datasets.SplitGenerator( | |
| #name=datasets.Split.TEST, | |
| #gen_kwargs={ | |
| #"data_file": os.path.join(dl_dir, f"{task_name}.jsonl"), | |
| #"split": datasets.Split.TEST, | |
| #}, | |
| #), | |
| #] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "data_file": os.path.join(dl_dir, "train.jsonl"), | |
| "split": datasets.Split.TRAIN, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "data_file": os.path.join(dl_dir, "validation.jsonl"), | |
| "split": datasets.Split.VALIDATION, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "data_file": os.path.join(dl_dir, "test.jsonl"), | |
| "split": datasets.Split.TEST, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, data_file, split): | |
| with open(data_file, encoding="utf-8") as f: | |
| for idx, line in enumerate(f): | |
| row = json.loads(line) | |
| question = row["query"] | |
| context = row['context'] | |
| options = [row[f"option_{i}"] for i in range(4)] | |
| label = options[row['label']] | |
| article_id = row["article_id"] | |
| difficulty = row['difficulty'] | |
| yield f"{self.config.name}-{split}-{idx}", { | |
| "context": context, | |
| "output": label, | |
| "options": options, | |
| "question": question, | |
| "article_id": article_id, | |
| "difficulty": difficulty | |
| } | |
| def _get_task_name_from_data_url(data_url): | |
| #setup = data_url.split("/")[-2] | |
| #agent = data_url.split("/")[-1].split("agent_")[-1] | |
| return data_url.split("/")[-1].split(".")[0] | |
| #first_flag = "first" in setup | |
| #maxlen = setup.split("maxlen")[-1] | |
| #return f"dpr-{'first' if first_flag else 'rest'}-{agent}%-maxlen-{maxlen}%" | |