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from utils.load_model import load_ner |
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from utils.input_process import make_ner_input |
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from utils.ner_utils import make_name_list, show_name_list, combine_similar_names |
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import torch |
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from utils.train_model import KCSN |
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from utils.arguments import get_train_args |
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args = get_train_args() |
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path ='model/model.ckpt' |
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model = KCSN(args) |
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checkpoint = torch.load(path) |
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model.load_state_dict(checkpoint['model']) |
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with open('test/test.txt', "r", encoding="utf-8") as f: |
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file_content = f.read() |
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content = make_ner_input(file_content) |
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name_list, time, place = make_name_list(content, checkpoint) |
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name_dic = show_name_list(name_list) |
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similar_name = combine_similar_names(name_dic) |
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for i in similar_name: |
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print(i) |
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import torch |
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from utils.fs_utils import get_alias2id, find_speak |
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from utils.ner_utils import make_name_list |
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from utils.input_process import make_ner_input, make_instance_list, input_data_loader |
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checkpoint = torch.load('./model/final.pth') |
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model = checkpoint['model'] |
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model.to('cpu') |
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tokenizer = checkpoint['tokenizer'] |
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check_name = './data/name.txt' |
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alias2id = get_alias2id(check_name) |
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with open('test/KoCSN_test.txt', "r", encoding="utf-8") as f: |
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file_content = f.read() |
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instances, instance_num = make_instance_list(file_content) |
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inputs = input_data_loader(instances, alias2id) |
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output = find_speak(model, inputs, tokenizer, alias2id) |
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def make_script(texts, instance_num, output): |
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script = [] |
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for idx, text in enumerate(texts): |
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if idx in instance_num |
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n = int(input()) |
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num = list(map(int, input().split())) |
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ans = [] |
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for i, j in enumerate(num): |
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print(i, j) |
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if len(ans) == 0: |
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ans.append(i+1) |
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else: |
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ans.insert(len(ans)-j, i+1) |
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print(ans) |
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