61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
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from read_data import TagIdConverter, make_long_namedEntity, readKoreanDataAll, Sentence
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from typing import Any, NamedTuple, List, Sequence, TypeVar
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import json
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import os.path as path
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import tqdm
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from transformers import PreTrainedTokenizer
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PREPROCESSING_BASE_PATH = 'prepro'
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converter = TagIdConverter()
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def preprocessing(tokenizer : PreTrainedTokenizer,dataset: List[Sentence]):
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ret = []
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for item in tqdm.tqdm(dataset):
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assert len(item.word) == len(item.detail)
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tokens = tokenizer.tokenize(" ".join(item.word))
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e = make_long_namedEntity(item.word,tokens,item.detail)
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if len(e) != len(tokens):
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print(e,tokens)
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assert len(e) == len(tokens)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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entityIds = converter.convert_tokens_to_ids(e)
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ret.append({"tokens":tokens,"ids":ids,"entity":e,"entity_ids": entityIds})
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return ret
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def saveObject(path: str,data: Any):
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with open(path,"w",encoding="utf-8") as fp:
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json.dump(data,fp,ensure_ascii=False, indent=2)
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def readPreprocessedData(path: str):
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with open(path,"r", encoding="utf-8") as fp:
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return json.load(fp)
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def readPreporcssedDataAll():
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train = readPreprocessedData(path.join(PREPROCESSING_BASE_PATH,"train.json"))
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dev = readPreprocessedData(path.join(PREPROCESSING_BASE_PATH,"dev.json"))
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test = readPreprocessedData(path.join(PREPROCESSING_BASE_PATH,"test.json"))
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return train, dev, test
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if __name__ == "__main__":
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from transformers import BertTokenizer
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PRETAINED_MODEL_NAME = 'bert-base-multilingual-cased'
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rawTrain, rawDev, rawTest = readKoreanDataAll()
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print("load tokenzier...")
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tokenizer = BertTokenizer.from_pretrained(PRETAINED_MODEL_NAME)
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print("process train...")
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train = preprocessing(tokenizer,rawTrain)
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saveObject(path.join(PREPROCESSING_BASE_PATH,"train.json"),train)
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print("process dev...")
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dev = preprocessing(tokenizer,rawDev)
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saveObject(path.join(PREPROCESSING_BASE_PATH,"dev.json"),dev)
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print("process test...")
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test = preprocessing(tokenizer,rawTest)
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saveObject(path.join(PREPROCESSING_BASE_PATH,"test.json"),test)
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