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# coding=utf-8
# 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.
"""Demo for pretrain"""
import os
import json
import datasets
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_LICENSE = "apache-license-2.0"
_HOMEPAGE = "https://github.com/IDEA-CCNL/Fengshenbang-LM"
class Config(datasets.BuilderConfig):
"""BuilderConfig for Demo"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
"""
Args:
**kwargs: keyword arguments forwarded to super.
"""
class PretrainCorpusDemo(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIG_CLASS = Config
BUILDER_CONFIGS = [
Config(description=_DESCRIPTION)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"text": datasets.Value("string"),
}),
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE
)
def _split_generators(self, dl_manager):
files = {
"test": os.path.join("data", f"train.json"),
"validation": os.path.join("data", f"train.json"),
"train": os.path.join("data", f"train.json"),
}
data_dir = dl_manager.download_and_extract(files)
output = []
test = datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir["test"]
}
)
output.append(test)
# if os.path.exists(data_dir["validation"]):
valid = datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir["validation"]
}
)
output.append(valid)
train = datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir["train"]
}
)
output.append(train)
return output
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
lines = f.readlines()
for id_, line in enumerate(lines):
data = json.loads(line)
s = {
'text': data['text'],
}
yield id_, s
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