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import os |
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import datasets |
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import json |
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_DESCRIPTION = """\ |
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LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion. |
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""" |
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_HOMEPAGE = "https://github.com/THUDM/LongBench" |
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_URL = r"https://huggingface.co/datasets/THUDM/LongBench/resolve/main/data.zip" |
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task_list = [ |
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"narrativeqa", |
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"qasper", |
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"multifieldqa_en", |
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"multifieldqa_zh", |
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"hotpotqa", |
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"2wikimqa", |
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"musique", |
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"dureader", |
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"gov_report", |
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"qmsum", |
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"multi_news", |
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"vcsum", |
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"trec", |
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"triviaqa", |
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"samsum", |
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"lsht", |
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"passage_count", |
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"passage_retrieval_en", |
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"passage_retrieval_zh", |
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"lcc", |
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"repobench-p", |
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"qasper_e", |
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"multifieldqa_en_e", |
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"hotpotqa_e", |
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"2wikimqa_e", |
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"gov_report_e", |
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"multi_news_e", |
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"trec_e", |
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"triviaqa_e", |
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"samsum_e", |
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"passage_count_e", |
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"passage_retrieval_en_e", |
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"lcc_e", |
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"repobench-p_e" |
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] |
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class LongBenchConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
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class LongBench(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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LongBenchConfig( |
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name=task_name, |
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) |
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for task_name in task_list |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"input": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"answers": [datasets.Value("string")], |
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"length": datasets.Value("int32"), |
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"dataset": datasets.Value("string"), |
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"language": datasets.Value("string"), |
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"all_classes": [datasets.Value("string")], |
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"_id": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(_URL) |
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task_name = self.config.name |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, "data", f"{task_name}.jsonl" |
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), |
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}, |
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) |
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] |
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def _generate_examples(self, filepath): |
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with open(filepath, encoding="utf-8") as f: |
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for idx, line in enumerate(f): |
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key = f"{self.config.name}-{idx}" |
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item = json.loads(line) |
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yield key, { |
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"input": item["input"], |
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"context": item["context"], |
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"answers": item["answers"], |
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"length": item["length"], |
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"dataset": item["dataset"], |
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"language": item["language"], |
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"_id": item["_id"], |
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"all_classes": item["all_classes"], |
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} |
