Datasets:
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import datasets
import json
_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"
_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"],
} |