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| import os |
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| import datasets |
| import json |
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| _DESCRIPTION = """\ |
| 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. |
| """ |
|
|
| _HOMEPAGE = "https://github.com/THUDM/LongBench" |
|
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|
|
| _URL = r"https://huggingface.co/datasets/THUDM/LongBench/resolve/main/data.zip" |
|
|
| task_list = [ |
| "narrativeqa", |
| "qasper", |
| "multifieldqa_en", |
| "multifieldqa_zh", |
| "hotpotqa", |
| "2wikimqa", |
| "musique", |
| "dureader", |
| "gov_report", |
| "qmsum", |
| "multi_news", |
| "vcsum", |
| "trec", |
| "triviaqa", |
| "samsum", |
| "lsht", |
| "passage_count", |
| "passage_retrieval_en", |
| "passage_retrieval_zh", |
| "lcc", |
| "repobench-p", |
| "qasper_e", |
| "multifieldqa_en_e", |
| "hotpotqa_e", |
| "2wikimqa_e", |
| "gov_report_e", |
| "multi_news_e", |
| "trec_e", |
| "triviaqa_e", |
| "samsum_e", |
| "passage_count_e", |
| "passage_retrieval_en_e", |
| "lcc_e", |
| "repobench-p_e" |
| ] |
|
|
|
|
| class LongBenchConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super().__init__(version=datasets.Version("1.0.0"), **kwargs) |
|
|
|
|
| class LongBench(datasets.GeneratorBasedBuilder): |
| BUILDER_CONFIGS = [ |
| LongBenchConfig( |
| name=task_name, |
| ) |
| for task_name in task_list |
| ] |
|
|
| def _info(self): |
| features = datasets.Features( |
| { |
| "input": datasets.Value("string"), |
| "context": datasets.Value("string"), |
| "answers": [datasets.Value("string")], |
| "length": datasets.Value("int32"), |
| "dataset": datasets.Value("string"), |
| "language": datasets.Value("string"), |
| "all_classes": [datasets.Value("string")], |
| "_id": datasets.Value("string"), |
| } |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| data_dir = dl_manager.download_and_extract(_URL) |
| task_name = self.config.name |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir, "data", f"{task_name}.jsonl" |
| ), |
| }, |
| ) |
| ] |
|
|
| def _generate_examples(self, filepath): |
| with open(filepath, encoding="utf-8") as f: |
| for idx, line in enumerate(f): |
| key = f"{self.config.name}-{idx}" |
| item = json.loads(line) |
| yield key, { |
| "input": item["input"], |
| "context": item["context"], |
| "answers": item["answers"], |
| "length": item["length"], |
| "dataset": item["dataset"], |
| "language": item["language"], |
| "_id": item["_id"], |
| "all_classes": item["all_classes"], |
| } |
