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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 csv
import json
import os
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ko-LIMA: Korean LIMA Dataset},
author={Hahn, Taeseung},
year={2023}
}
"""
_DESCRIPTION = """\
A high-quality korean dataset for efficient instruction tuning.
"""
_HOMEPAGE = ""
_LICENSE = ""
_URLS = {
"plain": "koLIMA-plain.zip", #
"vicuna": "koLIMA-vicuna.zip", #
}
class KoLima(datasets.GeneratorBasedBuilder):
"""A high-quality korean dataset for efficient instruction tuning."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="plain", version=VERSION, description="Korean LIMA dataset in a plain format"),
datasets.BuilderConfig(name="vicuna", version=VERSION, description="Korean LIMA dataset in Vicuna format"),
]
DEFAULT_CONFIG_NAME = "plain"
def _info(self):
if self.config.name == "vicuna":
features = datasets.Features(
{
'id': datasets.Value(dtype='string', id=None),
'conversations': [
{
'from': datasets.Value(dtype='string', id=None),
'value': datasets.Value(dtype='string', id=None)
}
]
}
)
else:
features = datasets.Features(
{
'conversations': datasets.Sequence(feature=datasets.Value(dtype='string', id=None), length=-1, id=None),
'source': datasets.Value(dtype='string', id=None)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.jsonl"),
"split": "train",
},
),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# gen_kwargs={
# "filepath": os.path.join(data_dir, "dev.jsonl"),
# "split": "dev",
# },
# ),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "test.jsonl"),
"split": "test"
},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
instance = json.loads(row)
if self.config.name == "vicuna":
yield key, instance
else:
yield key, instance |