mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import json
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import os
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from datasets import load_dataset
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dataset_id = "cnn_dailymail"
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dataset_config = "3.0.0"
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text_column = "article"
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summary_column = "highlights"
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instruction_template = "Summarize the following news article:"
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def preprocess_function(sample):
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# create list of samples
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inputs = [
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{"instruction": instruction_template, "input": text, "output": summary}
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for text, summary in zip(sample[text_column], sample[summary_column])
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]
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model_inputs = {"text": inputs}
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return model_inputs
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--save_path", type=str, default="data")
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return parser.parse_args()
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def main():
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args = parse_args()
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# Load dataset from the hub
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dataset = load_dataset(dataset_id, name=dataset_config)
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# process dataset
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tokenized_dataset = dataset.map(
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preprocess_function, batched=True, remove_columns=list(dataset["train"].features)
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)
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# save dataset to disk
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os.makedirs(args.save_path, exist_ok=True)
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with open(os.path.join(args.save_path, "cnn_train.json"), "w") as write_f:
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json.dump(tokenized_dataset["train"]["text"], write_f, indent=4, ensure_ascii=False)
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with open(os.path.join(args.save_path, "cnn_eval.json"), "w") as write_f:
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json.dump(tokenized_dataset["test"]["text"], write_f, indent=4, ensure_ascii=False)
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if __name__ == "__main__":
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main()
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