RedPajama-Data-1T / RedPajama-Data-1T.py
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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.
"""
_URL_LISTS = {
"arxiv": "urls/arxiv.txt",
"book": "urls/book.txt",
"c4": "urls/c4.txt",
"common_crawl": "urls/common_crawl.txt",
"github": "urls/github.txt",
"stackexchange": "urls/stackexchange.txt",
"wikipedia": "urls/wikipedia.txt",
}
class RedPajama1TConfig(datasets.BuilderConfig):
"""BuilderConfig for RedPajama sample."""
def __init__(self, *args, subsets, **kwargs):
"""BuilderConfig for RedPajama.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(RedPajama1TConfig, self).__init__(**kwargs)
self.subsets = subsets
class RedPajama1T(datasets.GeneratorBasedBuilder):
"""RedPajama: Reproducing the LLaMA training dataset of over 1.2 trillion tokens. Version 1.0.0."""
BUILDER_CONFIGS = [
RedPajama1TConfig(
subsets = list(_URL_LISTS.keys()),
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"),
"red_pajama_subset": datasets.Value("string"),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
url_lists = dl_manager.download_and_extract({
subset: _URL_LISTS[subset] for subset in self.config.subsets
})
urls = {}
for subset, url_list in url_lists.items():
with open(url_list, encoding="utf-8") as f:
urls[subset] = [line.strip() for line in f][:1]
downloaded_files = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs = {
"files": {
subset: downloaded_files[subset]
for subset in self.config.subsets
}
}
)
]
def _generate_examples(self, files):
"""This function returns the examples in the raw (text) form."""
key = 0
for subset in files:
if subset == "common_crawl":
import zstandard as zstd
for path in files[subset]:
with zstd.open(open(path, "rb"), "rt", encoding="utf-8") as f:
for i, row in enumerate(f):
data = json.loads(row)
text = data["text"]
del data["text"]
yield key, {
"text": text,
"meta": json.dumps(data),
"red_pajama_subset": subset,
}
key += 1
else:
for path in files[subset]:
with open(path, encoding="utf-8") as f:
for i, row in enumerate(f):
data = json.loads(row)
yield key, {
"text": data["text"],
"meta": data["meta"],
"red_pajama_subset": subset,
}
key += 1