34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
from datasets import DatasetDict
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from datasets import load_dataset, concatenate_datasets
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from modules.datapipeline import DataPipeline, 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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normalizer = TextNormalizer()
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query = normalizer.normalize(query)
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resp = normalizer.normalize(resp)
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return {"query": query, "response": resp }
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if __name__ == "__main__":
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all_data = ['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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datasets = []
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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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datasets.append(ds["train"])
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combined_dataset = concatenate_datasets(datasets)
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pipeline = DataPipeline()
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pipeline.process_dataset(
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dataset_dict=DatasetDict({"train": combined_dataset}),
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output_subdir="chinese-instruct-sft",
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process_func=process_func,
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) |