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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3f7659fd4834d9ceb5d72e2509ddbff9e9c75ca4aaffd02cc577957a3adaf59
+size 26195705

long_bench.py

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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/huayangli/long_bench/resolve/main/data.zip"
+
+task_list = ["narrativeqa", "qasper", "multifieldqa_en", "2wikimqa", "musique", \
+            "dureader", "gov_report", "qmsum", "multi_news", "vcsum", "trec", "triviaqa", "samsum", "lsht"]
+
+
+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"],
+                }