32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
from datasets import load_dataset, concatenate_datasets
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from pipeline import export_dataset, TextNormalizer
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normalizer = TextNormalizer()
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def process_func(input_dict: dict):
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query = input_dict["prompt"] if input_dict["prompt"] else ""
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resp = input_dict["response"] if input_dict["response"] else ""
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return {"query": normalizer.normalize(query), "response": normalizer.normalize(resp)}
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if __name__ == "__main__":
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all_data = [
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'stem_zh', 'infinity-instruct', 'firefly', 'magpie', 'dpsk-r1-distil',
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'coig-cqia', 'disc-law', 'neo_sft_phase2', 'chinese-medical', 'chinese-reasoning-distil',
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'psycho-10k-dpsk-r1', 'sof-c-zh', 'industryinstruction', 'Chinese-QA-AFAF',
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]
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dataset_list = []
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for subset in all_data:
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ds = load_dataset("Mxode/Chinese-Instruct", name=subset)
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dataset_list.append(ds["train"])
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combined_dataset = concatenate_datasets(dataset_list)
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export_dataset(
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dataset=combined_dataset,
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output_dir="./dataset",
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output_prefix="chinese-instruct-sft",
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process_func=process_func,
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)
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