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# Copyright 2023 Together Computer
#
# 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.
# Lint as: python3
"""RedPajama: An Open-Source, Clean-Room 1.2 Trillion Token Dataset."""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset.
"""
_URLS = [
"arxiv_sample.jsonl",
"book_sample.jsonl",
"c4_sample.jsonl",
"cc_2019-30_sample.jsonl",
"cc_2020-05_sample.jsonl",
"cc_2021-04_sample.jsonl",
"cc_2022-05_sample.jsonl",
"cc_2023-06_sample.jsonl",
"github_sample.jsonl",
"stackexchange_sample.jsonl",
"wikipedia_sample.jsonl",
]
class RedPajama1TSampleConfig(datasets.BuilderConfig):
"""BuilderConfig for RedPajama sample."""
def __init__(self, **kwargs):
"""BuilderConfig for RedPajama sample.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(RedPajama1TSampleConfig, self).__init__(**kwargs)
class RedPajama1TSample(datasets.GeneratorBasedBuilder):
"""RedPajama 1T Sample: version 1.0.0."""
BUILDER_CONFIGS = [
RedPajama1TSampleConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"meta": datasets.Value("string"),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files})
]
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepaths)
key = 0
for filepath in filepaths:
with open(filepath, encoding="utf-8") as f:
for row in f:
data = json.loads(row)
if "meta" not in data:
text = data["text"]
del data["text"]
yield key, {
"text": text,
"meta": json.dumps(data),
}
else:
yield key, {
"text": data["text"],
"meta": data["meta"],
}
key += 1
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