Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Sub-tasks:
language-modeling
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
License:
File size: 2,484 Bytes
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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 json
import os
import datasets
import pandas as pd
from datasets import DatasetInfo, DownloadManager
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_LICENSE = ""
_URL = r"https://huggingface.co/datasets/hkust-nlp/llm-compression/resolve/main/data.zip"
Task_list = [
"python",
"cc",
"arxiv_math",
]
class LlmCompressionConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
class LlmCompression(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
LlmCompressionConfig(
name=task_name,
)
for task_name in Task_list
]
def _info(self):
features = datasets.Features(
{
"content": datasets.Value("string"),
"subset": datasets.Value("string"),
"meta": datasets.features.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
license=_LICENSE,
)
def _split_generators(self, dl_manager: DownloadManager):
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, f"{task_name}.jsonl")})
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath,encoding="utf-8") as f:
for id_, row in enumerate(f):
cur_data = json.loads(row)
yield id_, {
"content": cur_data["content"],
"subset": cur_data["subset"],
"meta": str(cur_data["meta"]),
}
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