mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-11-09 09:14:17 +08:00
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
#
|
|
# 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 argparse
|
|
import os
|
|
from pathlib import Path
|
|
|
|
from datasets import load_dataset
|
|
|
|
|
|
def get_parser():
|
|
parser = argparse.ArgumentParser(description="Process nvidia/OpenScience dataset")
|
|
parser.add_argument("--output-dir", type=str, default=".")
|
|
return parser
|
|
|
|
|
|
def convert_row_oai(row: dict):
|
|
return {
|
|
"messages": [
|
|
{"role": "user", "content": row["input"]},
|
|
{"role": "assistant", "content": row["output"]},
|
|
]
|
|
}
|
|
|
|
|
|
def process_subset(raw_dir, proc_dir):
|
|
ds = load_dataset(raw_dir)
|
|
ds = ds.map(convert_row_oai, remove_columns=["input", "output"])
|
|
|
|
split_ds = ds["train"].train_test_split(test_size=0.1)
|
|
split_ds["train"].to_json(os.path.join(proc_dir, "training.jsonl"))
|
|
split_ds["test"].to_json(os.path.join(proc_dir, "validation.jsonl"))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = get_parser().parse_args()
|
|
raw_dir = f"{args.output_dir}/openscience_raw"
|
|
proc_dir = f"{args.output_dir}/openscience_proc"
|
|
|
|
if not os.path.exists(raw_dir):
|
|
q235_subset = load_dataset("nvidia/OpenScience", data_files="OS-Q3-235B-4.jsonl")
|
|
q235_subset.save_to_disk(raw_dir)
|
|
|
|
if not os.path.exists(proc_dir):
|
|
Path(proc_dir).mkdir(exist_ok=True)
|
|
print("Processing OpenScience dataset")
|
|
process_subset(raw_dir, proc_dir)
|
|
else:
|
|
print(f"Processed OpenScience dataset exists in: {proc_dir}, skipped processing")
|