├── .circleci └── config.yml ├── .gitignore ├── .vscode ├── extensions.json └── settings.json ├── CITATION.cff ├── LICENSE ├── README.md ├── configs ├── conv1-spacer-config.json ├── conv2-spacer-config.json └── conv3-spacer-config.json ├── pyproject.toml ├── quickspacer ├── __init__.py ├── constant.py ├── data.py ├── model.py ├── resources │ ├── default_saved_model │ │ ├── 1 │ │ │ ├── assets │ │ │ │ └── vocab.txt │ │ │ ├── saved_model.pb │ │ │ └── variables │ │ │ │ ├── variables.data-00000-of-00001 │ │ │ │ └── variables.index │ │ ├── 2 │ │ │ ├── assets │ │ │ │ └── vocab.txt │ │ │ ├── saved_model.pb │ │ │ └── variables │ │ │ │ ├── variables.data-00000-of-00001 │ │ │ │ └── variables.index │ │ └── 3 │ │ │ ├── assets │ │ │ └── vocab.txt │ │ │ ├── saved_model.pb │ │ │ └── variables │ │ │ ├── variables.data-00000-of-00001 │ │ │ └── variables.index │ └── vocab.txt ├── spacer.py └── utils.py ├── requirements-dev.txt ├── requirements.txt ├── scripts ├── check_time.py ├── convert_to_savedmodel.py ├── convert_to_tfjsmodel.py ├── make_vocab.py └── train_quickspacer.py ├── setup.py ├── tests ├── __init__.py ├── test_constant.py ├── test_data.py ├── test_spacer.py └── test_utils.py └── tfjs_models ├── model1 ├── group1-shard1of1.bin └── model.json ├── model2 ├── group1-shard1of2.bin ├── group1-shard2of2.bin └── model.json └── model3 ├── group1-shard1of3.bin ├── group1-shard2of3.bin ├── group1-shard3of3.bin └── model.json /.circleci/config.yml: -------------------------------------------------------------------------------- 1 | version: 2.1 2 | 3 | executors: 4 | python-executor: 5 | working_directory: ~/quickspacer 6 | docker: 7 | - image: circleci/python:3.8 8 | 9 | jobs: 10 | run-test: 11 | executor: python-executor 12 | steps: 13 | - checkout 14 | 15 | - restore_cache: 16 | key: deps-{{ checksum "requirements.txt" }}-{{ checksum "requirements-dev.txt" }} 17 | 18 | - run: 19 | name: Create Virtual Environment and Install Dependencies 20 | command: | 21 | virtualenv env 22 | source env/bin/activate 23 | pip install -r requirements.txt -r requirements-dev.txt 24 | 25 | - save_cache: 26 | key: deps-{{ checksum "requirements.txt" }}-{{ checksum "requirements-dev.txt" }} 27 | paths: 28 | - "env" 29 | 30 | - run: 31 | name: Run Tests 32 | command: | 33 | source env/bin/activate 34 | pytest --cov-branch --cov quickspacer 35 | 36 | - run: 37 | name: Upload coverage report 38 | command: | 39 | source env/bin/activate 40 | codecov 41 | 42 | check-linting: 43 | executor: python-executor 44 | steps: 45 | - checkout 46 | 47 | - restore_cache: 48 | key: deps-{{ checksum "requirements.txt" }}-{{ checksum "requirements-dev.txt" }} 49 | 50 | - run: 51 | name: Create Virtual Environment and Install Dependencies 52 | command: | 53 | virtualenv env 54 | source env/bin/activate 55 | pip install -r requirements.txt -r requirements-dev.txt 56 | 57 | - save_cache: 58 | key: deps-{{ checksum "requirements.txt" }}-{{ checksum "requirements-dev.txt" }} 59 | paths: 60 | - "env" 61 | 62 | - run: 63 | name: Run black, isort 64 | command: | 65 | source env/bin/activate 66 | black --check quickspacer scripts tests 67 | isort quickspacer scripts tests 68 | 69 | workflows: 70 | main: 71 | jobs: 72 | - run-test 73 | - check-linting 74 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # Environments 7 | .env 8 | .venv 9 | env/ 10 | venv/ 11 | ENV/ 12 | env.bak/ 13 | venv.bak/ 14 | 15 | # personal vscode settings 16 | .vscode/launch.json 17 | .vscode/tasks.json 18 | 19 | # log file 20 | *.log 21 | 22 | # script output files 23 | saved_spacer_model/ 24 | tfjs_spacer_model/ 25 | -------------------------------------------------------------------------------- /.vscode/extensions.json: -------------------------------------------------------------------------------- 1 | { 2 | "recommendations": [ 3 | "ms-python.python" 4 | ] 5 | } 6 | -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "python.pythonPath": "env/bin/python3.7", 3 | "python.formatting.provider": "black", 4 | "editor.formatOnSave": true, 5 | "files.trimTrailingWhitespace": true, 6 | "files.insertFinalNewline": true 7 | } 8 | -------------------------------------------------------------------------------- /CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 1.2.0 2 | type: generic 3 | message: "If you use this code, please cite this as below." 4 | authors: 5 | - family-names: "Park" 6 | given-names: "Sangjun" 7 | orcid: "https://orcid.org/0000-0002-1838-9259" 8 | title: "quickspacer" 9 | version: 1.0.4 10 | date-released: 2022-11-16 11 | url: "https://github.com/cosmoquester/quickspacer" 12 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 ParkSangJun 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # QuickSpacer 2 | 3 | [![codecov](https://codecov.io/gh/cosmoquester/quickspacer/branch/master/graph/badge.svg)](https://codecov.io/gh/cosmoquester/quickspacer) 4 | 5 | 6 | - 빠른 속도와 준수한 정확도를 목표로하는 한국어 띄어쓰기 교정 모델입니다. 7 | - 이 레포지토리에 있는 모델들은 [모두의 말뭉치](https://corpus.korean.go.kr) `국립국어원 문어 말뭉치(버전 1.0)` 데이터를 이용하여 학습한 모델입니다. 8 | 9 | # Demo 10 | 11 | 데모는 Tensorflow JS로 만들어져 있으며 https://cosmoquester.github.io/quickspacer/ 에서 사용해보실 수 있습니다. 12 | 13 | # Install & Usage 14 | 15 | ```bash 16 | pip install quickspacer 17 | ``` 18 | 위 명령어로 설치하실 수 있습니다. 19 | 20 | ```python 21 | from quickspacer import Spacer 22 | 23 | spacer = Spacer() 24 | spacer.space(["띄어쓰기를안한나쁜말", "또는 띄어쓰기가 잘 되어있는 좋은 말"]) 25 | spacer.space(["띄어쓰기를안한나쁜말", "또는 띄어쓰기가 잘 되어있는 좋은 말", ...], batch_size=48) 26 | ``` 27 | 이런식으로 사용하실 수 있습니다. spacer.space() 함수는 띄어쓰기가 된 리스트를 반환합니다. 28 | 29 | batch_size옵션을 사용하면 batch단위로 묶여 연산합니다. 30 | batch내에 있는 문장들은 가장 긴 문장길이를 기준으로 나머지 문장은 padding하여 연산을 진행하므로 batch_size가 너무 크고 길이가 엄청 긴 문장이 들어있다면 전체 추론 속도가 느려질 수 있습니다. 31 | batch_size를 따로 넘기지 않으면 입력 데이터 전체를 한 번에 계산합니다. 32 | 33 | ```python 34 | from quickspacer import Spacer 35 | 36 | spacer = Spacer("somewhere/my_custom_savedmodel_dir") 37 | spacer = Spacer(saved_model_dir="somewhere/my_custom_savedmodel_dir") 38 | ``` 39 | 만약 모델을 따로 학습시키셨다면, 위와 같이 saved_model 경로를 인자로 넘겨 직접학습한 모델을 사용할 수 있습니다. 40 | 41 | 물론 savedmodel이 레포지토리의 scripts.convert_to_savedmodel 스크립트를 통해 변환된 경우만 가능합니다. 42 | 43 | ```python 44 | from quickspacer import Spacer 45 | 46 | # Level 1 Default, Same as Spacer() 47 | spacer1 = Spacer(level=1) 48 | # Level 2 49 | spacer2 = Spacer(level=2) 50 | # Level 3 51 | spacer1 = Spacer(level=3) 52 | ``` 53 | Spacer의 인스턴스를 만들 때 위와 같이 level 인자를 넘겨줄 수 있습니다. (기본: 1) 레벨은 높을수록 일반적인 띄어쓰기 성능이 향상되며 대신 속도가 더 오래걸립니다. 54 | 55 | # Train 56 | 57 | ## Make Vocab 58 | 59 | ```bash 60 | python -m scripts.make_vocab \ 61 | --input-dir [corpus_directory_path] \ 62 | --vocab-path [vocab_file_path] 63 | ``` 64 | 기본 vocab은 quickspacer/resources/vocab.txt에 존재하지만 따로 문자열이 필요하거나 다른 언어의 띄어쓰기 모델을 만들 예정이라면 위 명령어를 통해 새로 vocab 파일을 만들 수 있습니다. 65 | 66 | ## Model Train 67 | 68 | ```bash 69 | python -m scripts.train_quickspacer \ 70 | --model-name [model_name] \ 71 | --dataset-file-path [dataset_paths] \ 72 | --output-path [output_dir_path] 73 | ``` 74 | 모델은 위 명령어로 학습할 수 있습니다. 75 | - 현재 레포지토리에 존재하는 모델은 [ConvSpacer1, ConvSpacer2, ConvSpacer3] 세 종류입니다. model-name에는 이 세 가지 중 하나를 입력합니다. 76 | - 각 모델마다 사용하는 파라미터가 있는데 configs 디렉토리에 기본 설정파일들이 있으며 이를 수정해서 사용하면 됩니다. 77 | - dataset은 UTF-8로 인코딩 된 텍스트파일 형태입니다. 띄어쓰기가 올바르게 되어있는 문장이라고 가정하고 학습합니다. 여러 파일을 입력할 수 있습니다. ex) "corpus_*.txt" 78 | 79 | ```bash 80 | python -m scripts.train_quickspacer --help 81 | ``` 82 | 를 보면 여러 학습 parameter을 입력할 수 있습니다. 83 | 84 | # Deploy 85 | 86 | 배포는 SavedModel을 이용한 배포와 Tensorflowjs를 이용한 배포가 가능합니다. 87 | 88 | ## Deploy using SavedModel 89 | 90 | ### Make SavedModel 91 | 92 | ```bash 93 | python -m scripts.convert_to_savedmodel \ 94 | --model-weight-path [model_weight_path] \ 95 | --output-path [saved_model_path] 96 | ``` 97 | 위 명령어를 통해 모델을 TF SavedModel 형식으로 변환할 수 있습니다. 98 | - model-weight-path는 train을 통해서 models에 저장된 경로를 입력하면 되는데 "spacer-XXepoch-xxx.index" 이런 식으로 파일이 존재하는데 "spacer-XXepoch-xxx" 까지만 입력합니다. 99 | - SavedModel은 그대로 Tensorflow로 Load해서 그대로 사용해도 되고, Tensorflow serving 등을 통해 API 서버로 Deploy할 수 있습니다. 100 | 101 | ### SavedModel Additional Description 102 | 103 | convert_to_savedmodel로 변환한 모델은 두 개의 signature function을 가지고 있습니다. 아래 명령들은 SavedModel을 만드는 것과는 무관하며 추가적인 설명을 위한 것입니다. 104 | ```bash 105 | $ saved_model_cli show --dir saved_spacer_model/1 \ 106 | --tag_set serve \ 107 | --signature_def serving_default 108 | 2020-11-15 17:02:30.861944: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 109 | The given SavedModel SignatureDef contains the following input(s): 110 | inputs['texts'] tensor_info: 111 | dtype: DT_STRING 112 | shape: (-1) 113 | name: serving_default_texts:0 114 | The given SavedModel SignatureDef contains the following output(s): 115 | outputs['spaced_sentences'] tensor_info: 116 | dtype: DT_STRING 117 | shape: unknown_rank 118 | name: StatefulPartitionedCall_1:0 119 | Method name is: tensorflow/serving/predict 120 | ``` 121 | - default는 text를 입력받고 띄어쓰기가 완료된 문장을 반환하도록 되어있습니다. 위의 saved_model_cli를 통해 살펴보면 DT_STRING이 입출력인 것을 알 수 있습니다. 122 | 123 | ```bash 124 | $ saved_model_cli run --dir saved_spacer_model/1 \ 125 | --tag_set serve \ 126 | --signature_def serving_default \ 127 | --input_exprs 'texts=["근데이것좀띄워주시겠어요?", "싫은데영ㅎㅎ"]' 128 | 2020-11-15 17:06:27.735637: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 129 | 2020-11-15 17:06:28.659347: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1 130 | [각종 TF log들 ...] 131 | Result for output key spaced_sentences: 132 | [b'\xea\xb7\xbc\xeb\x8d\xb0 \xec\x9d\xb4\xea\xb2\x83 \xec\xa2\x80 \xeb\x9d\x84\xec\x9b\x8c \xec\xa3\xbc\xec\x8b\x9c\xea\xb2\xa0\xec\x96\xb4\xec\x9a\x94?' 133 | b'\xec\x8b\xab\xec\x9d\x80\xeb\x8d\xb0 \xec\x98\x81\xe3\x85\x8e\xe3\x85\x8e'] 134 | ``` 135 | - 이런 식으로 saved_model이 잘 저장되었고 제대로 동작하는지 확인할 수 있습니다. 136 | - Unicode 바이너리로 나와서 조금 불편한데 한글로 바꿔보면 ["근데 이것 좀 띄워 주시겠어요?","싫은데 영ㅎㅎ"] 으로 띄어쓰기를 해주었습니다. 137 | 138 | ```bash 139 | $ saved_model_cli show --dir saved_spacer_model/1 \ 140 | --tag_set serve \ 141 | --signature_def model_inference 142 | 2020-11-15 17:03:19.988061: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 143 | The given SavedModel SignatureDef contains the following input(s): 144 | inputs['tokens'] tensor_info: 145 | dtype: DT_INT32 146 | shape: (-1, -1) 147 | name: model_inference_tokens:0 148 | The given SavedModel SignatureDef contains the following output(s): 149 | outputs['output_0'] tensor_info: 150 | dtype: DT_FLOAT 151 | shape: (-1, -1) 152 | name: StatefulPartitionedCall:0 153 | Method name is: tensorflow/serving/predict 154 | ``` 155 | - 또 하나의 Signature 함수는 문장을 글자를 다 잘라서 Vocab을 이용해 숫자로 변환된 입력을 받고, 각 자리를 띄워야할 확률을 알려줍니다. 156 | - 아까와 같은 문장을 숫자로 변환하여 테스트해보겠습니다. 157 | 158 | ```bash 159 | $ saved_model_cli run --dir saved_spacer_model/1 \ 160 | --tag_set serve \ 161 | --signature_def model_inference \ 162 | --input_exprs 'tokens=[[88,26,4,100,112,1241,93,64,38,56, 6,19,15],[216,33,26,202,67,67,0,0,0,0,0,0,0]]' 163 | [각종 Tensorflow log...] 164 | Result for output key output_0: 165 | [[2.5608379e-03 9.8520654e-01 1.2721949e-03 9.7731644e-01 9.9997485e-01 166 | 1.6742251e-07 5.0763595e-01 2.1732522e-03 1.0649196e-03 2.6994228e-04 167 | 1.1066308e-04 1.4717710e-03 2.8897190e-01] 168 | [3.6909140e-04 1.2601367e-02 8.4685940e-01 7.0986725e-06 1.3404434e-05 169 | 5.6068022e-10 2.8169470e-12 1.1617506e-17 2.8605146e-17 2.8605146e-17 170 | 2.8605146e-17 5.3611558e-16 2.1768996e-07]] 171 | ``` 172 | - 두 문장의 길이가 다른 경우에는 위 예시처럼 0으로 패딩을 해줘야합니다. 173 | - 결과를 보면 각 위치마다 띄어야할 확률이 나왔습니다. 174 | 175 | ### Deploy using Tensorflow/serving docker 176 | 177 | ```bash 178 | $ docker run --rm --name test -p 8500:8500 -p 8501:8501 \ 179 | --mount type=bind,source=`pwd`/saved_spacer_model,target=/models/spacer \ 180 | -e MODEL_NAME=spacer \ 181 | -t tensorflow/serving:latest 182 | ``` 183 | 간단히 docker로 tensorflow serving 서버를 여는 명령입니다. 현재 모델 파일은 실제로는 `pwd`/saved_1pacer_model/1 에 저장되어 있습니다. 184 | 185 | ```bash 186 | $ curl -X POST localhost:8501/v1/models/spacer:predict \ 187 | -H "Content-Type: application/json" \ 188 | -d '{"instances":["근데이것좀띄워주시겠어요!", "싫은데영ㅎㅎ"]}' 189 | { 190 | "predictions": ["근데 이것 좀 띄워 주시겠어요!", "싫은데 영ㅎㅎ" 191 | ] 192 | } 193 | ``` 194 | 이제 curl을 이용해 테스트해보면 정상적으로 띄어쓰기가 완료된 문장을 반환하는 것을 볼 수 있습니다. signature function을 지정하면 model_inference만 하는 것도 가능합니다. 195 | 196 | ## Deploy using Tensorflowjs 197 | 198 | 다음은 tensorflowjs를 이용해서 서버가 아닌 클라이언트의 브라우저에서 추론하도록할 수 있습니다. 데모페이지에 있는 것도 Tensorflow JS를 이용한 것입니다. 199 | 200 | ### Make TFJS Graph Model 201 | 202 | ```bash 203 | python -m scripts.convert_to_tfjsmodel \ 204 | --saved-model-path [saved_model_path] \ 205 | --output-path [output_dir_path] 206 | ``` 207 | 이 명령어를 통해 Tensorflow JS 모델로 변환할 수 있습니다. 208 | TFJS에서도 문장을 넣고 문장이 나오도록 만들고 싶었지만 Vocab을 포함하고 있는 signature function은 TFJS로 변환하는데 에러가 발생하여 TFJS에선 모델 추론만 하도록 했습니다. 209 | 위 파이썬 스크립트를 이용하지 않더라도 `tensorflowjs_wizard`나 `tensorflowjs_converter` 명령어를 바로 사용해도 됩니다. 210 | 211 | ### Use Tensorflow JS Graph Model 212 | 213 | - https://js.tensorflow.org/api/latest/ 를 참고하면 js로 사용할 수 있는 API가 정리되어 있습니다. 214 | - tfjs를 사용하려면 https://www.tensorflow.org/js/tutorials/setup에 나와있는 것처럼 tensorflow js 파일을 넣어줘야합니다. 215 | 216 | 우리가 위에서 만든 건 tf.GraphModel이므로 `tf.loadGraphModel` 함수로 불러와서 사용하면 됩니다. 217 | -------------------------------------------------------------------------------- /configs/conv1-spacer-config.json: -------------------------------------------------------------------------------- 1 | { 2 | "embedding_dim": 64, 3 | "hidden_dim": 128, 4 | "kernel_size": 9, 5 | "dropout": 0.2 6 | } 7 | -------------------------------------------------------------------------------- /configs/conv2-spacer-config.json: -------------------------------------------------------------------------------- 1 | { 2 | "embedding_dim": 64, 3 | "hidden_dim": 256, 4 | "kernel_size1": 5, 5 | "kernel_size2": 11, 6 | "dropout": 0.3 7 | } 8 | -------------------------------------------------------------------------------- /configs/conv3-spacer-config.json: -------------------------------------------------------------------------------- 1 | { 2 | "embedding_dim": 128, 3 | "hidden_dim": 256, 4 | "kernel_size1": 5, 5 | "kernel_size2": 7, 6 | "kernel_size3": 11, 7 | "kernel_size4": 5, 8 | "dropout": 0.3 9 | } 10 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.black] 2 | line-length = 120 3 | include = '\.pyi?$' 4 | 5 | [tool.isort] 6 | multi_line_output = 3 7 | line_length = 120 8 | include_trailing_comma = true 9 | -------------------------------------------------------------------------------- /quickspacer/__init__.py: -------------------------------------------------------------------------------- 1 | from . import constant, data, model, utils 2 | from .spacer import Spacer 3 | 4 | __all__ = ["constant", "data", "model", "utils", "Spacer"] 5 | -------------------------------------------------------------------------------- /quickspacer/constant.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import tensorflow as tf 4 | 5 | RESOURCE_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), "resources")) 6 | DEFAULT_VOCAB_PATH = os.path.join(RESOURCE_PATH, "vocab.txt") 7 | DEFAULT_MODEL_PATH = os.path.join(RESOURCE_PATH, "default_saved_model") 8 | DEFAULT_OOV_INDEX = tf.constant(1, dtype=tf.int32) 9 | DEFAULT_SPACE_INDEX = tf.constant(2, dtype=tf.int32) 10 | with open(DEFAULT_VOCAB_PATH, encoding="utf-8") as f: 11 | DEFAULT_VOCAB_SIZE = len(list(f)) 12 | -------------------------------------------------------------------------------- /quickspacer/data.py: -------------------------------------------------------------------------------- 1 | import random 2 | from functools import partial 3 | from typing import Tuple, Union 4 | 5 | import tensorflow as tf 6 | 7 | from .constant import DEFAULT_OOV_INDEX, DEFAULT_SPACE_INDEX, DEFAULT_VOCAB_PATH 8 | 9 | 10 | def get_dataset( 11 | dataset_file_path: str, 12 | min_remove_rate: float, 13 | max_remove_rate: float, 14 | num_parallel_reads: int = 4, 15 | num_parallel_calls: int = 4, 16 | vocab_file_path: str = DEFAULT_VOCAB_PATH, 17 | oov_index: Union[tf.Tensor, int] = DEFAULT_OOV_INDEX, 18 | space_index: Union[tf.Tensor, int] = DEFAULT_SPACE_INDEX, 19 | ) -> tf.data.Dataset: 20 | """ 21 | Read dataset file and construct tensorflow dataset 22 | 23 | :param dataset_file_path: dataset (txt) file path. 24 | :param min_remove_rate: minimum rate to remove spaces of all spaces. 25 | :param max_remove_rate: maximum rate to remove spaces of all spaces. 26 | :param num_parallel_reads: number for parallel reading 27 | :param num_parallel_calls: number for parallel mapping 28 | :param vocab_file_path: vocab file path. 29 | :param oov_index: OOV index. 30 | :param space_index: " " character index in vocab. 31 | """ 32 | vocab = load_vocab(vocab_file_path, oov_index) 33 | dataset = ( 34 | tf.data.TextLineDataset(dataset_file_path, num_parallel_reads=num_parallel_reads) 35 | .map(partial(sentence_to_index, vocab=vocab), num_parallel_calls=num_parallel_calls) 36 | .map( 37 | partial( 38 | sentence_to_dataset, 39 | min_remove_rate=min_remove_rate, 40 | max_remove_rate=max_remove_rate, 41 | space_index=space_index, 42 | ), 43 | num_parallel_calls=num_parallel_calls, 44 | ) 45 | ) 46 | return dataset 47 | 48 | 49 | def sentence_to_index(sentence: tf.Tensor, vocab: tf.lookup.StaticHashTable) -> tf.Tensor: 50 | """ 51 | Mapping character to number with vocab 52 | 53 | :param sentence: string type 0D (scalar) tensor of sentence. 54 | :param vocab: vocab loaded using 'load_vocab'. 55 | """ 56 | mapped = vocab.lookup(tf.strings.unicode_split(sentence, "UTF-8")) 57 | return tf.cast(mapped, tf.int32) 58 | 59 | 60 | def sentence_to_dataset( 61 | token_ords: tf.Tensor, 62 | min_remove_rate: Union[float, tf.Tensor], 63 | max_remove_rate: Union[float, tf.Tensor], 64 | space_index=DEFAULT_SPACE_INDEX, 65 | ) -> Tuple[tf.Tensor, tf.Tensor]: 66 | """ 67 | Remove spaces in tokens by remove_rate 68 | 69 | :param token_ords: int type 1D tensor of a sentence shaped (SequenceLength). 70 | :param min_remove_rate: minimum rate to remove spaces of all spaces. 71 | :param max_remove_rate: maximum rate to remove spaces of all spaces. 72 | :param space_index: vocab index of space " ". 73 | :return: tuple of input and label. Both shapes are (SequenceLengthAfterRemoveSpace) 74 | """ 75 | remove_rate = tf.random.uniform((), min_remove_rate, max_remove_rate) 76 | space_indices = tf.where(token_ords == space_index)[:, 0] 77 | num_removed_spaces = tf.cast(tf.math.ceil(tf.cast(tf.size(space_indices), tf.float32) * remove_rate), tf.int32) 78 | removed_space_indices = tf.random.shuffle(space_indices)[:num_removed_spaces] 79 | token_ords = tf.tensor_scatter_nd_update( 80 | token_ords, removed_space_indices[:, tf.newaxis], tf.fill(tf.shape(removed_space_indices), -1) 81 | ) 82 | label_indices = tf.sort(removed_space_indices) - tf.range(1, tf.size(removed_space_indices) + 1, dtype=tf.int64) 83 | labels = tf.scatter_nd( 84 | label_indices[:, tf.newaxis], tf.ones(num_removed_spaces, tf.int32), [tf.size(token_ords) - num_removed_spaces] 85 | ) 86 | # In case space is consecutive (ex "안 녕") and remove consecutive space 87 | # labels can be value more than 1, so prevent that situation 88 | labels = tf.where(labels <= 1, labels, 1) 89 | 90 | return tf.gather(token_ords, tf.where(token_ords != -1))[:, 0], labels 91 | 92 | 93 | def load_vocab(vocab_file_path: str, oov_index: Union[int, tf.Tensor]) -> tf.lookup.StaticHashTable: 94 | """ 95 | Load vocab from file 96 | """ 97 | if isinstance(oov_index, int): 98 | oov_index = tf.constant(oov_index, tf.int64) 99 | if isinstance(oov_index, tf.Tensor): 100 | oov_index = tf.cast(oov_index, tf.int64) 101 | 102 | vocab = tf.lookup.StaticHashTable( 103 | tf.lookup.TextFileInitializer( 104 | vocab_file_path, 105 | key_dtype=tf.string, 106 | key_index=tf.lookup.TextFileIndex.WHOLE_LINE, 107 | value_dtype=tf.int64, 108 | value_index=tf.lookup.TextFileIndex.LINE_NUMBER, 109 | delimiter="\n", 110 | ), 111 | default_value=oov_index, 112 | ) 113 | return vocab 114 | -------------------------------------------------------------------------------- /quickspacer/model.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | from .constant import DEFAULT_VOCAB_SIZE 4 | 5 | 6 | class ConvSpacer1(tf.keras.Model): 7 | def __init__(self, embedding_dim, hidden_dim, kernel_size, dropout, vocab_size=DEFAULT_VOCAB_SIZE): 8 | super(ConvSpacer1, self).__init__() 9 | 10 | self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) 11 | self.conv = tf.keras.layers.Conv1D(hidden_dim, kernel_size, activation="relu", padding="same") 12 | self.dropout = tf.keras.layers.Dropout(dropout) 13 | self.dense = tf.keras.layers.Conv1D(1, 3, padding="same") 14 | 15 | def call(self, inputs, training=None): 16 | # Batch x SequenceLength x EmbeddingDim 17 | output = self.embedding(inputs) 18 | 19 | # Batch x SequenceLength x HiddenDim 20 | output = self.conv(output) 21 | 22 | # Batch x SequenceLength 23 | output = self.dense(self.dropout(output))[:, :, 0] 24 | output = tf.nn.sigmoid(output) 25 | 26 | return output 27 | 28 | 29 | class ConvSpacer2(tf.keras.Model): 30 | def __init__(self, embedding_dim, hidden_dim, kernel_size1, kernel_size2, dropout, vocab_size=DEFAULT_VOCAB_SIZE): 31 | super(ConvSpacer2, self).__init__() 32 | 33 | self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) 34 | 35 | self.conv1 = tf.keras.layers.Conv1D(hidden_dim, kernel_size1, activation="relu", padding="same") 36 | self.conv2 = tf.keras.layers.Conv1D(hidden_dim, kernel_size2, activation="relu", padding="same") 37 | self.dropout = tf.keras.layers.Dropout(dropout) 38 | 39 | self.dense = tf.keras.layers.Conv1D(1, 3, padding="same") 40 | 41 | def call(self, inputs, training=None): 42 | # Batch x SequenceLength x EmbeddingDim 43 | embedded = self.embedding(inputs) 44 | 45 | # Batch x SequenceLength x HiddenDim 46 | output = self.conv1(embedded) 47 | 48 | # Batch x SequenceLength x (EmbeddingDim + HiddenDim) 49 | # Cast to tf.float32, because of mixed precision 50 | output = tf.concat([tf.cast(embedded, tf.float32), tf.cast(output, tf.float32)], axis=2) 51 | 52 | # Batch x SequenceLength x HiddenDim 53 | output = self.conv2(output) 54 | 55 | # Batch x SequenceLength 56 | output = self.dense(self.dropout(output))[:, :, 0] 57 | output = tf.nn.sigmoid(output) 58 | 59 | return output 60 | 61 | 62 | class ConvSpacer3(tf.keras.Model): 63 | def __init__( 64 | self, 65 | embedding_dim, 66 | hidden_dim, 67 | kernel_size1, 68 | kernel_size2, 69 | kernel_size3, 70 | kernel_size4, 71 | dropout, 72 | vocab_size=DEFAULT_VOCAB_SIZE, 73 | ): 74 | super(ConvSpacer3, self).__init__() 75 | 76 | self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) 77 | 78 | self.conv1 = tf.keras.layers.Conv1D(hidden_dim, kernel_size1, activation="relu", padding="same") 79 | self.conv2 = tf.keras.layers.Conv1D(hidden_dim, kernel_size2, activation="relu", padding="same") 80 | self.conv3 = tf.keras.layers.Conv1D(hidden_dim, kernel_size3, activation="relu", padding="same") 81 | self.dropout = tf.keras.layers.Dropout(dropout) 82 | 83 | self.conv4 = tf.keras.layers.Conv1D(hidden_dim, kernel_size4, activation="relu", padding="same") 84 | self.dense = tf.keras.layers.Conv1D(1, 3, padding="same") 85 | 86 | def call(self, inputs, training=None): 87 | # Batch x SequenceLength x EmbeddingDim 88 | embedded = self.embedding(inputs) 89 | 90 | # Batch x SequenceLength x HiddenDim 91 | output1 = self.conv1(embedded) 92 | output2 = self.conv2(embedded) 93 | output3 = self.conv3(embedded) 94 | 95 | # Batch x SequenceLength x (EmbeddingDim + HiddenDim x 3) 96 | # Cast to tf.float32, because of mixed precision 97 | output = tf.concat( 98 | [ 99 | tf.cast(embedded, tf.float32), 100 | tf.cast(output1, tf.float32), 101 | tf.cast(output2, tf.float32), 102 | tf.cast(output3, tf.float32), 103 | ], 104 | axis=2, 105 | ) 106 | output = self.dropout(output) 107 | 108 | # Batch x SequenceLength x HiddenDim 109 | output = self.conv4(output) 110 | 111 | # Batch x SequenceLength 112 | output = self.dense(output)[:, :, 0] 113 | output = tf.nn.sigmoid(output) 114 | 115 | return output 116 | -------------------------------------------------------------------------------- /quickspacer/resources/default_saved_model/1/assets/vocab.txt: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ㅋ 5 | 이 6 | 아 7 | 어 8 | 고 9 | 가 10 | 그 11 | 다 12 | 지 13 | 나 14 | 는 15 | 하 16 | ? 17 | 도 18 | 서 19 | . 20 | 요 21 | 기 22 | 야 23 | 해 24 | 자 25 | 에 26 | 니 27 | 데 28 | 게 29 | 안 30 | 거 31 | 오 32 | 면 33 | 리 34 | 은 35 | 내 36 | 라 37 | 있 38 | 사 39 | 시 40 | 래 41 | 보 42 | 일 43 | 만 44 | 까 45 | 네 46 | 한 47 | 으 48 | 구 49 | 로 50 | 을 51 | 좋 52 | 너 53 | 먹 54 | 제 55 | 마 56 | 랑 57 | 겠 58 | 들 59 | 무 60 | 여 61 | 말 62 | 응 63 | 진 64 | 었 65 | 주 66 | 했 67 | 우 68 | ㅎ 69 | 러 70 | ! 71 | 려 72 | 수 73 | 뭐 74 | 잘 75 | 대 76 | 인 77 | 금 78 | 정 79 | 같 80 | ㅠ 81 | 부 82 | 없 83 | 음 84 | 왜 85 | 더 86 | 알 87 | 저 88 | 생 89 | 근 90 | 난 91 | 짜 92 | 할 93 | 미 94 | 워 95 | 늘 96 | 전 97 | 히 98 | 모 99 | 바 100 | 럼 101 | 것 102 | 스 103 | 많 104 | 간 105 | 건 106 | 애 107 | 상 108 | 싶 109 | 웅 110 | 와 111 | 렇 112 | 빠 113 | 좀 114 | 귀 115 | 되 116 | 예 117 | 집 118 | 심 119 | 냥 120 | 냐 121 | 못 122 | ~ 123 | 때 124 | 았 125 | 화 126 | 치 127 | 를 128 | 쪽 129 | , 130 | 런 131 | 장 132 | 르 133 | 중 134 | 연 135 | 비 136 | 의 137 | 봐 138 | 맞 139 | 각 140 | 신 141 | 돼 142 | 당 143 | 조 144 | 소 145 | 분 146 | 원 147 | ㅜ 148 | 찮 149 | 디 150 | 공 151 | 람 152 | 피 153 | 잖 154 | 두 155 | 괜 156 | 언 157 | 날 158 | 실 159 | 테 160 | 뽀 161 | 세 162 | 배 163 | 동 164 | 직 165 | 않 166 | 걸 167 | 1 168 | 친 169 | 운 170 | 누 171 | 방 172 | 왔 173 | 갈 174 | 얼 175 | 번 176 | 남 177 | 엄 178 | 님 179 | 줄 180 | 단 181 | 학 182 | 잠 183 | 드 184 | 밥 185 | 행 186 | 물 187 | 형 188 | 끝 189 | 든 190 | 울 191 | 른 192 | 터 193 | 속 194 | 재 195 | 머 196 | 용 197 | 입 198 | 줘 199 | 헤 200 | 문 201 | 트 202 | 약 203 | 영 204 | 져 205 | 청 206 | 발 207 | 교 208 | 버 209 | 살 210 | 개 211 | 경 212 | 힘 213 | 올 214 | 민 215 | 계 216 | 노 217 | 싫 218 | 쁘 219 | 또 220 | 몰 221 | 따 222 | 맛 223 | 긴 224 | 적 225 | 차 226 | 유 227 | 파 228 | 군 229 | 졸 230 | 감 231 | 엉 232 | 프 233 | 선 234 | 불 235 | 놀 236 | 찍 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862 | 삭 863 | 쩡 864 | 벼 865 | 쩐 866 | 떼 867 | 팩 868 | 압 869 | 샘 870 | 팡 871 | 힌 872 | 탱 873 | 뀌 874 | 켓 875 | 깡 876 | 욥 877 | 렴 878 | 쉴 879 | 혔 880 | 핫 881 | 쨔 882 | 얔 883 | 흘 884 | 캬 885 | 갓 886 | 닦 887 | 렷 888 | 럭 889 | 뚝 890 | 츠 891 | 낙 892 | ㅗ 893 | ️ 894 | 💕 895 | 뚱 896 | 탓 897 | 힐 898 | 캡 899 | 츄 900 | 빛 901 | 헛 902 | 닫 903 | 왱 904 | 웬 905 | 혯 906 | 뻥 907 | 뷰 908 | 꾼 909 | 쿵 910 | 꼈 911 | 롤 912 | ㅐ 913 | 옵 914 | 튀 915 | 뀨 916 | 숭 917 | 썽 918 | 묶 919 | 춤 920 | 짤 921 | 밝 922 | 갸 923 | 딨 924 | 쁨 925 | 찐 926 | 므 927 | 쫄 928 | 듬 929 | 멈 930 | 잌 931 | 깊 932 | 갠 933 | ㄲ 934 | 겅 935 | 얻 936 | 찔 937 | 셈 938 | 읍 939 | 릭 940 | 릎 941 | 례 942 | 끗 943 | 납 944 | 붕 945 | 맹 946 | 쥐 947 | 얍 948 | 칫 949 | 깍 950 | ㅔ 951 | 앵 952 | 늙 953 | 틈 954 | 헬 955 | 혐 956 | 핳 957 | 엘 958 | 싼 959 | 넓 960 | 촉 961 | s 962 | 뚜 963 | 컵 964 | 셀 965 | 녔 966 | 닠 967 | 땠 968 | 뜯 969 | 폐 970 | 땅 971 | 밋 972 | 땀 973 | 햐 974 | 좌 975 | 협 976 | 굉 977 | 끓 978 | 튜 979 | 뜩 980 | 넴 981 | 툰 982 | 삶 983 | 뮤 984 | 힣 985 | 셋 986 | 잣 987 | 흡 988 | 녜 989 | 왤 990 | 꼼 991 | 봣 992 | 쌓 993 | > 994 | 헝 995 | 율 996 | 뺏 997 | 룩 998 | 엎 999 | 겜 1000 | 낳 1001 | 닼 1002 | 꼴 1003 | 칙 1004 | 뻤 1005 | 릇 1006 | 껏 1007 | 뒀 1008 | 벨 1009 | 훗 1010 | 쩍 1011 | 굶 1012 | 낄 1013 | 쾌 1014 | 휘 1015 | 랍 1016 | 헹 1017 | 닙 1018 | 뎈 1019 | 뜬 1020 | 룰 1021 | 슝 1022 | 웹 1023 | 혈 1024 | 넼 1025 | 픔 1026 | 쌈 1027 | 쳇 1028 | 탑 1029 | 흰 1030 | 뒈 1031 | 홈 1032 | 됬 1033 | 긁 1034 | 쵸 1035 | 앤 1036 | 훔 1037 | 묘 1038 | 셧 1039 | 뽕 1040 | 짘 1041 | 뇌 1042 | < 1043 | 둔 1044 | 객 1045 | 췄 1046 | 컸 1047 | ㅊ 1048 | 컷 1049 | 땜 1050 | 끙 1051 | 듀 1052 | ㅆ 1053 | 긔 1054 | 샐 1055 | 눕 1056 | 뱃 1057 | 엠 1058 | 죵 1059 | 톱 1060 | 꽉 1061 | 놉 1062 | 겐 1063 | 홉 1064 | 왼 1065 | 얀 1066 | 썩 1067 | 쌩 1068 | 먄 1069 | 찝 1070 | 콤 1071 | 긋 1072 | 틴 1073 | 졋 1074 | 헌 1075 | 줭 1076 | 률 1077 | 곸 1078 | 힛 1079 | 좁 1080 | 넝 1081 | 뎌 1082 | 뭉 1083 | 낀 1084 | 샵 1085 | 랰 1086 | 훌 1087 | 썸 1088 | 싹 1089 | 펴 1090 | 멜 1091 | @ 1092 | ㅃ 1093 | 꽂 1094 | 짬 1095 | 찢 1096 | 껭 1097 | 꽁 1098 | 뿡 1099 | 벤 1100 | 윗 1101 | 봅 1102 | 킁 1103 | 닿 1104 | 얄 1105 | ( 1106 | 뷔 1107 | 웟 1108 | 닷 1109 | 컬 1110 | 밴 1111 | # 1112 | 굼 1113 | 떴 1114 | 눠 1115 | 촬 1116 | 얇 1117 | 귄 1118 | 닉 1119 | 핵 1120 | 웨 1121 | 셩 1122 | 껐 1123 | ㅛ 1124 | 됩 1125 | 썹 1126 | 겄 1127 | 쮸 1128 | ㅍ 1129 | 봥 1130 | 펜 1131 | 펑 1132 | 덤 1133 | ㅑ 1134 | 렛 1135 | 틱 1136 | 뚫 1137 | 픽 1138 | 😘 1139 | 냅 1140 | 팝 1141 | 뺨 1142 | 퀴 1143 | 값 1144 | 쟝 1145 | 😍 1146 | 턱 1147 | 겉 1148 | 띵 1149 | c 1150 | 즁 1151 | 밉 1152 | 젊 1153 | 퉁 1154 | 셉 1155 | m 1156 | 섬 1157 | 쫓 1158 | 쩜 1159 | 눙 1160 | 숫 1161 | 뱅 1162 | 랏 1163 | 숑 1164 | 깻 1165 | 샷 1166 | / 1167 | 델 1168 | % 1169 | 콧 1170 | 햌 1171 | * 1172 | 깎 1173 | a 1174 | 뼈 1175 | 뻗 1176 | 팍 1177 | 쬬 1178 | 쉿 1179 | 램 1180 | 됏 1181 | = 1182 | 덟 1183 | 😉 1184 | 샌 1185 | 싯 1186 | 겻 1187 | 빗 1188 | 꿍 1189 | 핥 1190 | 텨 1191 | 댄 1192 | 쏟 1193 | 빔 1194 | 갱 1195 | 캠 1196 | 뜸 1197 | 볍 1198 | 템 1199 | 텀 1200 | 뤄 1201 | 랙 1202 | " 1203 | 홧 1204 | 엿 1205 | 돋 1206 | 킴 1207 | 낯 1208 | 략 1209 | 핀 1210 | 렉 1211 | 깬 1212 | 괘 1213 | 떵 1214 | 맑 1215 | 껀 1216 | 💗 1217 | 꾹 1218 | 뱉 1219 | 옳 1220 | 컹 1221 | ㄸ 1222 | 붓 1223 | 읭 1224 | 맴 1225 | 낑 1226 | 갤 1227 | 섞 1228 | 삿 1229 | 쪘 1230 | 뒹 1231 | 캔 1232 | 묜 1233 | 껍 1234 | 랩 1235 | + 1236 | 깃 1237 | 굽 1238 | 밟 1239 | 쬭 1240 | 욬 1241 | 렬 1242 | 띄 1243 | 튕 1244 | 컥 1245 | 룽 1246 | ㅉ 1247 | p 1248 | 낰 1249 | 둑 1250 | 앨 1251 | 귤 1252 | 넉 1253 | 욘 1254 | o 1255 | t 1256 | 칸 1257 | 팟 1258 | 엣 1259 | 웤 1260 | 씌 1261 | 쫍 1262 | 빅 1263 | 낚 1264 | 맵 1265 | 뇸 1266 | 틸 1267 | 줍 1268 | 랗 1269 | 넨 1270 | 븐 1271 | 욧 1272 | 팸 1273 | 흉 1274 | 쇠 1275 | A 1276 | 즉 1277 | 릅 1278 | 럿 1279 | v 1280 | 텍 1281 | 뢰 1282 | 곁 1283 | T 1284 | 쉰 1285 | 측 1286 | 돔 1287 | 썰 1288 | 뜽 1289 | 룡 1290 | 쩝 1291 | 랔 1292 | 띨 1293 | 씽 1294 | 꺅 1295 | 뺐 1296 | i 1297 | 멸 1298 | 댐 1299 | 됫 1300 | 륭 1301 | 핰 1302 | 쳣 1303 | 섴 1304 | 퉤 1305 | 긱 1306 | 왴 1307 | 냨 1308 | 똘 1309 | 뿔 1310 | 줫 1311 | 뭇 1312 | 꼽 1313 | 훅 1314 | 밧 1315 | 퍽 1316 | d 1317 | 헣 1318 | b 1319 | 잎 1320 | e 1321 | 댱 1322 | 뉘 1323 | 됑 1324 | 😊 1325 | 꽈 1326 | 콕 1327 | ㅕ 1328 | 룬 1329 | 벙 1330 | 뺄 1331 | 뜰 1332 | 짖 1333 | 쀼 1334 | 됴 1335 | 댈 1336 | 숩 1337 | 갘 1338 | 멤 1339 | 겪 1340 | 옼 1341 | 걘 1342 | B 1343 | 곀 1344 | 궈 1345 | 푼 1346 | S 1347 | 쏙 1348 | 쌌 1349 | 벚 1350 | 늑 1351 | 핏 1352 | 즌 1353 | 갚 1354 | 냇 1355 | 롯 1356 | g 1357 | 딧 1358 | 펐 1359 | 쑥 1360 | 삽 1361 | 얏 1362 | 팜 1363 | 귈 1364 | 깰 1365 | 뿜 1366 | 슉 1367 | 칩 1368 | 펀 1369 | 늠 1370 | 셤 1371 | 뱌 1372 | 찼 1373 | 윙 1374 | 쨩 1375 | k 1376 | 쫑 1377 | 찹 1378 | 뾰 1379 | 땨 1380 | 겤 1381 | 😌 1382 | 껌 1383 | 갰 1384 | 닳 1385 | 폴 1386 | … 1387 | 쇄 1388 | 몫 1389 | 앓 1390 | 쨋 1391 | 쨍 1392 | 썬 1393 | 놋 1394 | 씁 1395 | 늬 1396 | x 1397 | 흫 1398 | 뀐 1399 | 댁 1400 | 듦 1401 | z 1402 | 깥 1403 | 옙 1404 | 💞 1405 | 뵈 1406 | 샴 1407 | 펭 1408 | 쉐 1409 | 빳 1410 | 깽 1411 | 훙 1412 | 돠 1413 | 꼰 1414 | 뻘 1415 | 툴 1416 | 렐 1417 | 톤 1418 | 쥴 1419 | 💓 1420 | 첩 1421 | 뻑 1422 | 캄 1423 | 쯔 1424 | 껑 1425 | 썅 1426 | 뵙 1427 | 킄 1428 | 듕 1429 | 숲 1430 | 뎃 1431 | 쿼 1432 | 먀 1433 | 솜 1434 | 딪 1435 | 뭣 1436 | 큭 1437 | C 1438 | 슥 1439 | 풉 1440 | 쭝 1441 | 섰 1442 | 봇 1443 | 깤 1444 | 쬐 1445 | 폈 1446 | 툭 1447 | 꿇 1448 | 몹 1449 | 돕 1450 | 팽 1451 | 풋 1452 | 엮 1453 | 넥 1454 | 빚 1455 | 뎁 1456 | 쿡 1457 | 썻 1458 | 쎠 1459 | M 1460 | 팥 1461 | 쎈 1462 | 왈 1463 | D 1464 | ㆍ 1465 | n 1466 | 젛 1467 | 떄 1468 | 쏭 1469 | 룹 1470 | 읏 1471 | 읎 1472 | 둡 1473 | 샹 1474 | 쏠 1475 | 덧 1476 | 둠 1477 | 엊 1478 | 댘 1479 | 엨 1480 | 냔 1481 | 콱 1482 | 닛 1483 | 렙 1484 | 갯 1485 | 쐬 1486 | 츤 1487 | 홓 1488 | 얹 1489 | 숴 1490 | 😙 1491 | 롸 1492 | 옄 1493 | 뀜 1494 | 걀 1495 | 짴 1496 | 햤 1497 | 렜 1498 | 떳 1499 | 굵 1500 | 엏 1501 | 퐁 1502 | 췌 1503 | 뗘 1504 | 뺀 1505 | 솥 1506 | 셜 1507 | 쉅 1508 | 젝 1509 | 퍄 1510 | 횟 1511 | 쌔 1512 | 헴 1513 | 💋 1514 | 맣 1515 | 돗 1516 | 흙 1517 | 핡 1518 | 팁 1519 | 쭤 1520 | 😁 1521 | 깄 1522 | 숍 1523 | 얜 1524 | 뀔 1525 | 짭 1526 | G 1527 | 믹 1528 | 뭥 1529 | 놧 1530 | ☆ 1531 | 앟 1532 | 횡 1533 | l 1534 | 츈 1535 | 😗 1536 | 텼 1537 | 갇 1538 | 꺾 1539 | 읔 1540 | 젓 1541 | 뎀 1542 | 젼 1543 | 됭 1544 | 퀘 1545 | 헙 1546 | 웩 1547 | f 1548 | P 1549 | 홋 1550 | 떽 1551 | 챱 1552 | r 1553 | 렘 1554 | 옶 1555 | 잨 1556 | 챠 1557 | 쟨 1558 | X 1559 | 밭 1560 | O 1561 | 슐 1562 | 앖 1563 | 💜 1564 | 뱀 1565 | 삘 1566 | 탠 1567 | 얗 1568 | 🏻 1569 | 짚 1570 | 왛 1571 | 폼 1572 | 힉 1573 | 몀 1574 | 펌 1575 | 숏 1576 | 킼 1577 | 떰 1578 | 딥 1579 | 깠 1580 | 펙 1581 | 뽁 1582 | 왁 1583 | 켯 1584 | ㄳ 1585 | 괄 1586 | 뵤 1587 | 쭁 1588 | 훠 1589 | 텝 1590 | 귓 1591 | 킵 1592 | 맠 1593 | ] 1594 | 눼 1595 | 😭 1596 | 텅 1597 | 눅 1598 | 맺 1599 | 펄 1600 | 멎 1601 | u 1602 | 햏 1603 | 깈 1604 | 왘 1605 | 갛 1606 | 쓱 1607 | ☺ 1608 | 솟 1609 | 혓 1610 | 칵 1611 | 빕 1612 | 슙 1613 | h 1614 | 샥 1615 | 헥 1616 | w 1617 | 쒸 1618 | 뚠 1619 | 쨘 1620 | 녱 1621 | 깁 1622 | 츕 1623 | 젯 1624 | 듭 1625 | 뤘 1626 | 싳 1627 | [ 1628 | 훼 1629 | 뻣 1630 | 훟 1631 | 짰 1632 | 갬 1633 | 씸 1634 | 앱 1635 | 븅 1636 | ᆞ 1637 | 퀄 1638 | 웰 1639 | 칰 1640 | 먕 1641 | 셍 1642 | 😚 1643 | 맏 1644 | 넛 1645 | 꾜 1646 | 쫙 1647 | L 1648 | K 1649 | 쏴 1650 | 힙 1651 | 꿧 1652 | 킥 1653 | 뀽 1654 | V 1655 | 늉 1656 | 햅 1657 | 흣 1658 | 캣 1659 | 쥰 1660 | 앰 1661 | 촤 1662 | 웁 1663 | 잏 1664 | 뮬 1665 | 넫 1666 | 괌 1667 | 귱 1668 | ❣ 1669 | 쫀 1670 | 궄 1671 | 덴 1672 | 뎡 1673 | 꺌 1674 | 쾅 1675 | 뫄 1676 | 꼇 1677 | 쟀 1678 | 탭 1679 | ︎ 1680 | ᆢ 1681 | 샜 1682 | 딕 1683 | 돜 1684 | 싈 1685 | \ 1686 | 땄 1687 | 벳 1688 | 😆 1689 | 럈 1690 | 쉼 1691 | 멱 1692 | 멧 1693 | 밸 1694 | 뽐 1695 | 꽝 1696 | 멕 1697 | 😝 1698 | 챡 1699 | 헠 1700 | 😂 1701 | 랠 1702 | 즙 1703 | 펼 1704 | 겔 1705 | 빽 1706 | 얽 1707 | 펠 1708 | 숟 1709 | 뤼 1710 | 뺑 1711 | 톨 1712 | 잭 1713 | 갗 1714 | 뿍 1715 | 뉸 1716 | 꽥 1717 | 쯧 1718 | 볕 1719 | 빰 1720 | y 1721 | 삥 1722 | 륙 1723 | 쏜 1724 | 꾀 1725 | 뜌 1726 | 짢 1727 | 👍 1728 | R 1729 | 밬 1730 | 😋 1731 | 뢔 1732 | 띡 1733 | 겈 1734 | 쿰 1735 | 뺘 1736 | 붉 1737 | 땃 1738 | 봨 1739 | 얕 1740 | 둣 1741 | 낟 1742 | 큥 1743 | 꿉 1744 | 뚤 1745 | 뎨 1746 | 튭 1747 | 렼 1748 | 딤 1749 | 졐 1750 | 슘 1751 | 퉷 1752 | 륨 1753 | 끽 1754 | 잦 1755 | 쟌 1756 | 퐈 1757 | 젱 1758 | 윌 1759 | 쇽 1760 | 벡 1761 | 및 1762 | 쫘 1763 | 맄 1764 | 밈 1765 | 뤠 1766 | 챗 1767 | 읶 1768 | 윰 1769 | 휠 1770 | 륜 1771 | 듈 1772 | 핼 1773 | 췟 1774 | 짆 1775 | 뭡 1776 | Z 1777 | 읊 1778 | 쭘 1779 | 펫 1780 | 얶 1781 | 눴 1782 | 겝 1783 | 켁 1784 | 렄 1785 | 힠 1786 | I 1787 | 캉 1788 | 쟐 1789 | 묭 1790 | 꼐 1791 | 핮 1792 | 쩨 1793 | 뛴 1794 | 첸 1795 | • 1796 | 쌰 1797 | 😄 1798 | 팰 1799 | 🙈 1800 | 겧 1801 | 쥼 1802 | 😢 1803 | 😅 1804 | 쭐 1805 | ★ 1806 | 챔 1807 | 뛸 1808 | 멩 1809 | 퀸 1810 | 캭 1811 | 쓔 1812 | 핟 1813 | 롴 1814 | 깸 1815 | 셰 1816 | 얒 1817 | 뷴 1818 | 😳 1819 | 쯰 1820 | 겆 1821 | 룜 1822 | 놬 1823 | 쌉 1824 | 춍 1825 | 슌 1826 | 헨 1827 | 꿰 1828 | 끠 1829 | H 1830 | 웈 1831 | 맷 1832 | 쟉 1833 | 춝 1834 | 쨜 1835 | 톰 1836 | 잰 1837 | 넬 1838 | 엡 1839 | 킷 1840 | 앀 1841 | 퀵 1842 | 😏 1843 | 꿋 1844 | 댑 1845 | 큣 1846 | 똔 1847 | 됌 1848 | 큔 1849 | 💛 1850 | 씼 1851 | 🙏 1852 | 샛 1853 | 겎 1854 | 햣 1855 | 땟 1856 | 픕 1857 | 넜 1858 | 뭄 1859 | ㅝ 1860 | 💙 1861 | 찻 1862 | 켬 1863 | 앍 1864 | 젭 1865 | 춉 1866 | 즛 1867 | 틋 1868 | F 1869 | 앎 1870 | 쥥 1871 | 랖 1872 | 붐 1873 | 얐 1874 | 씐 1875 | j 1876 | U 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2099 | 😞 2100 | 꿩 2101 | 왐 2102 | 큘 2103 | 갹 2104 | 펏 2105 | 쨰 2106 | 뼛 2107 | 잽 2108 | 휑 2109 | 욀 2110 | 쒀 2111 | 걐 2112 | 😛 2113 | 갼 2114 | 챕 2115 | 솨 2116 | 댠 2117 | 덱 2118 | 핍 2119 | 뉨 2120 | 뀰 2121 | ㅘ 2122 | 읅 2123 | 럄 2124 | 삔 2125 | 쎀 2126 | 캇 2127 | 둿 2128 | 펨 2129 | 퐝 2130 | 닊 2131 | 쿜 2132 | 뉵 2133 | 뭠 2134 | 읻 2135 | 🙆 2136 | 왭 2137 | 윅 2138 | 꼿 2139 | ㅙ 2140 | 멓 2141 | 🐶 2142 | 졈 2143 | 쟛 2144 | 탤 2145 | 톧 2146 | 옜 2147 | 믾 2148 | 휭 2149 | 갴 2150 | 쬠 2151 | 싄 2152 | 갭 2153 | 뾱 2154 | 옝 2155 | 쟘 2156 | 춌 2157 | 떫 2158 | 앝 2159 | 쁩 2160 | 찯 2161 | 찧 2162 | 옘 2163 | 맢 2164 | 쥽 2165 | 휜 2166 | 컾 2167 | 퓌 2168 | 헵 2169 | 긑 2170 | 챈 2171 | 냣 2172 | 왑 2173 | 섀 2174 | 폿 2175 | 콸 2176 | 귐 2177 | 릏 2178 | 푠 2179 | 끛 2180 | ‍ 2181 | 놩 2182 | 쿸 2183 | 쿙 2184 | 믓 2185 | 귯 2186 | 빸 2187 | 쟙 2188 | 횬 2189 | 춧 2190 | 잒 2191 | 힁 2192 | 삣 2193 | 뷱 2194 | 렣 2195 | 닏 2196 | 짯 2197 | 츰 2198 | 깉 2199 | 끵 2200 | 좊 2201 | 빻 2202 | 좃 2203 | 녓 2204 | 쐈 2205 | 햬 2206 | 젆 2207 | 귿 2208 | 캎 2209 | 😔 2210 | 캥 2211 | 똫 2212 | 텁 2213 | 칮 2214 | 퍈 2215 | 먁 2216 | 👋 2217 | 캘 2218 | W 2219 | 랳 2220 | 뺙 2221 | 젔 2222 | 냡 2223 | 뭍 2224 | 븜 2225 | 뵌 2226 | 쏼 2227 | 뽝 2228 | 컼 2229 | 껰 2230 | ` 2231 | ㅒ 2232 | 둨 2233 | 쉈 2234 | 🤗 2235 | 콰 2236 | 쨕 2237 | 젴 2238 | 먐 2239 | 읒 2240 | 긌 2241 | 얖 2242 | 쎔 2243 | 헸 2244 | 퐉 2245 | 죰 2246 | 굥 2247 | 곶 2248 | 듷 2249 | 텟 2250 | 쳨 2251 | 땍 2252 | 셬 2253 | 💟 2254 | 햨 2255 | 늕 2256 | 햊 2257 | 🙌 2258 | 앸 2259 | 뜀 2260 | 낍 2261 | 촐 2262 | 뱡 2263 | 챴 2264 | 빋 2265 | 껜 2266 | 잗 2267 | 😻 2268 | 😒 2269 | 😑 2270 | 뵜 2271 | 낞 2272 | 뫃 2273 | ¡ 2274 | 큨 2275 | 눚 2276 | 쐐 2277 | 쯀 2278 | 졓 2279 | 듄 2280 | 쑴 2281 | 샾 2282 | 죙 2283 | 곗 2284 | 뒁 2285 | 핱 2286 | 뒙 2287 | 밯 2288 | 띁 2289 | 츼 2290 | q 2291 | 캴 2292 | 뽈 2293 | 봑 2294 | 웋 2295 | 몃 2296 | | 2297 | 핶 2298 | 칡 2299 | 츨 2300 | 폄 2301 | 밪 2302 | 럐 2303 | 👶 2304 | 🌙 2305 | 쥑 2306 | 껒 2307 | 죳 2308 | 짲 2309 | 쪜 2310 | 뉍 2311 | 쮹 2312 | 줒 2313 | 듐 2314 | 쳉 2315 | 넿 2316 | 퉈 2317 | 졀 2318 | 럌 2319 | 쓩 2320 | 얃 2321 | 궨 2322 | 뎠 2323 | 횐 2324 | 낡 2325 | 츅 2326 | 탇 2327 | 괭 2328 | ㅟ 2329 | 숖 2330 | 짼 2331 | 듁 2332 | 🎶 2333 | 랒 2334 | 샆 2335 | 죸 2336 | 밐 2337 | 녠 2338 | 꿱 2339 | 쮜 2340 | 쿗 2341 | 늫 2342 | 볻 2343 | 뱍 2344 | 웍 2345 | 햘 2346 | 줴 2347 | 몄 2348 | 툐 2349 | “ 2350 | ” 2351 | 옾 2352 | 캅 2353 | 탯 2354 | 슛 2355 | 헑 2356 | 첼 2357 | ‘ 2358 | 쯍 2359 | 묽 2360 | 뙇 2361 | 싐 2362 | 엳 2363 | 헀 2364 | 늄 2365 | 좨 2366 | 😠 2367 | 뮴 2368 | 럇 2369 | 뇬 2370 | 섷 2371 | 땈 2372 | 뤈 2373 | 췃 2374 | 꽇 2375 | 흌 2376 | 🌸 2377 | 좡 2378 | 🔥 2379 | } 2380 | 쉭 2381 | 😡 2382 | 줅 2383 | 춪 2384 | 먗 2385 | 닁 2386 | 뉜 2387 | 😪 2388 | 🐥 2389 | 🙄 2390 | 랸 2391 | 슺 2392 | 샨 2393 | × 2394 | 왂 2395 | 햿 2396 | 샅 2397 | 곻 2398 | 퓽 2399 | 햠 2400 | 쒯 2401 | 녬 2402 | 텡 2403 | 웂 2404 | 떢 2405 | 숰 2406 | 뷁 2407 | 렂 2408 | 쎼 2409 | 뺭 2410 | 뚕 2411 | 댬 2412 | 짔 2413 | 챵 2414 | 끕 2415 | 꽌 2416 | 괵 2417 | 귕 2418 | 푱 2419 | 껠 2420 | 졷 2421 | 폇 2422 | 굡 2423 | 팼 2424 | 딛 2425 | 늨 2426 | 딬 2427 | 얺 2428 | 찟 2429 | 👉 2430 | 👈 2431 | ⭐ 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| 픔 1026 | 쌈 1027 | 쳇 1028 | 탑 1029 | 흰 1030 | 뒈 1031 | 홈 1032 | 됬 1033 | 긁 1034 | 쵸 1035 | 앤 1036 | 훔 1037 | 묘 1038 | 셧 1039 | 뽕 1040 | 짘 1041 | 뇌 1042 | < 1043 | 둔 1044 | 객 1045 | 췄 1046 | 컸 1047 | ㅊ 1048 | 컷 1049 | 땜 1050 | 끙 1051 | 듀 1052 | ㅆ 1053 | 긔 1054 | 샐 1055 | 눕 1056 | 뱃 1057 | 엠 1058 | 죵 1059 | 톱 1060 | 꽉 1061 | 놉 1062 | 겐 1063 | 홉 1064 | 왼 1065 | 얀 1066 | 썩 1067 | 쌩 1068 | 먄 1069 | 찝 1070 | 콤 1071 | 긋 1072 | 틴 1073 | 졋 1074 | 헌 1075 | 줭 1076 | 률 1077 | 곸 1078 | 힛 1079 | 좁 1080 | 넝 1081 | 뎌 1082 | 뭉 1083 | 낀 1084 | 샵 1085 | 랰 1086 | 훌 1087 | 썸 1088 | 싹 1089 | 펴 1090 | 멜 1091 | @ 1092 | ㅃ 1093 | 꽂 1094 | 짬 1095 | 찢 1096 | 껭 1097 | 꽁 1098 | 뿡 1099 | 벤 1100 | 윗 1101 | 봅 1102 | 킁 1103 | 닿 1104 | 얄 1105 | ( 1106 | 뷔 1107 | 웟 1108 | 닷 1109 | 컬 1110 | 밴 1111 | # 1112 | 굼 1113 | 떴 1114 | 눠 1115 | 촬 1116 | 얇 1117 | 귄 1118 | 닉 1119 | 핵 1120 | 웨 1121 | 셩 1122 | 껐 1123 | ㅛ 1124 | 됩 1125 | 썹 1126 | 겄 1127 | 쮸 1128 | ㅍ 1129 | 봥 1130 | 펜 1131 | 펑 1132 | 덤 1133 | ㅑ 1134 | 렛 1135 | 틱 1136 | 뚫 1137 | 픽 1138 | 😘 1139 | 냅 1140 | 팝 1141 | 뺨 1142 | 퀴 1143 | 값 1144 | 쟝 1145 | 😍 1146 | 턱 1147 | 겉 1148 | 띵 1149 | c 1150 | 즁 1151 | 밉 1152 | 젊 1153 | 퉁 1154 | 셉 1155 | m 1156 | 섬 1157 | 쫓 1158 | 쩜 1159 | 눙 1160 | 숫 1161 | 뱅 1162 | 랏 1163 | 숑 1164 | 깻 1165 | 샷 1166 | / 1167 | 델 1168 | % 1169 | 콧 1170 | 햌 1171 | * 1172 | 깎 1173 | a 1174 | 뼈 1175 | 뻗 1176 | 팍 1177 | 쬬 1178 | 쉿 1179 | 램 1180 | 됏 1181 | = 1182 | 덟 1183 | 😉 1184 | 샌 1185 | 싯 1186 | 겻 1187 | 빗 1188 | 꿍 1189 | 핥 1190 | 텨 1191 | 댄 1192 | 쏟 1193 | 빔 1194 | 갱 1195 | 캠 1196 | 뜸 1197 | 볍 1198 | 템 1199 | 텀 1200 | 뤄 1201 | 랙 1202 | " 1203 | 홧 1204 | 엿 1205 | 돋 1206 | 킴 1207 | 낯 1208 | 략 1209 | 핀 1210 | 렉 1211 | 깬 1212 | 괘 1213 | 떵 1214 | 맑 1215 | 껀 1216 | 💗 1217 | 꾹 1218 | 뱉 1219 | 옳 1220 | 컹 1221 | ㄸ 1222 | 붓 1223 | 읭 1224 | 맴 1225 | 낑 1226 | 갤 1227 | 섞 1228 | 삿 1229 | 쪘 1230 | 뒹 1231 | 캔 1232 | 묜 1233 | 껍 1234 | 랩 1235 | + 1236 | 깃 1237 | 굽 1238 | 밟 1239 | 쬭 1240 | 욬 1241 | 렬 1242 | 띄 1243 | 튕 1244 | 컥 1245 | 룽 1246 | ㅉ 1247 | p 1248 | 낰 1249 | 둑 1250 | 앨 1251 | 귤 1252 | 넉 1253 | 욘 1254 | o 1255 | t 1256 | 칸 1257 | 팟 1258 | 엣 1259 | 웤 1260 | 씌 1261 | 쫍 1262 | 빅 1263 | 낚 1264 | 맵 1265 | 뇸 1266 | 틸 1267 | 줍 1268 | 랗 1269 | 넨 1270 | 븐 1271 | 욧 1272 | 팸 1273 | 흉 1274 | 쇠 1275 | A 1276 | 즉 1277 | 릅 1278 | 럿 1279 | v 1280 | 텍 1281 | 뢰 1282 | 곁 1283 | T 1284 | 쉰 1285 | 측 1286 | 돔 1287 | 썰 1288 | 뜽 1289 | 룡 1290 | 쩝 1291 | 랔 1292 | 띨 1293 | 씽 1294 | 꺅 1295 | 뺐 1296 | i 1297 | 멸 1298 | 댐 1299 | 됫 1300 | 륭 1301 | 핰 1302 | 쳣 1303 | 섴 1304 | 퉤 1305 | 긱 1306 | 왴 1307 | 냨 1308 | 똘 1309 | 뿔 1310 | 줫 1311 | 뭇 1312 | 꼽 1313 | 훅 1314 | 밧 1315 | 퍽 1316 | d 1317 | 헣 1318 | b 1319 | 잎 1320 | e 1321 | 댱 1322 | 뉘 1323 | 됑 1324 | 😊 1325 | 꽈 1326 | 콕 1327 | ㅕ 1328 | 룬 1329 | 벙 1330 | 뺄 1331 | 뜰 1332 | 짖 1333 | 쀼 1334 | 됴 1335 | 댈 1336 | 숩 1337 | 갘 1338 | 멤 1339 | 겪 1340 | 옼 1341 | 걘 1342 | B 1343 | 곀 1344 | 궈 1345 | 푼 1346 | S 1347 | 쏙 1348 | 쌌 1349 | 벚 1350 | 늑 1351 | 핏 1352 | 즌 1353 | 갚 1354 | 냇 1355 | 롯 1356 | g 1357 | 딧 1358 | 펐 1359 | 쑥 1360 | 삽 1361 | 얏 1362 | 팜 1363 | 귈 1364 | 깰 1365 | 뿜 1366 | 슉 1367 | 칩 1368 | 펀 1369 | 늠 1370 | 셤 1371 | 뱌 1372 | 찼 1373 | 윙 1374 | 쨩 1375 | k 1376 | 쫑 1377 | 찹 1378 | 뾰 1379 | 땨 1380 | 겤 1381 | 😌 1382 | 껌 1383 | 갰 1384 | 닳 1385 | 폴 1386 | … 1387 | 쇄 1388 | 몫 1389 | 앓 1390 | 쨋 1391 | 쨍 1392 | 썬 1393 | 놋 1394 | 씁 1395 | 늬 1396 | x 1397 | 흫 1398 | 뀐 1399 | 댁 1400 | 듦 1401 | z 1402 | 깥 1403 | 옙 1404 | 💞 1405 | 뵈 1406 | 샴 1407 | 펭 1408 | 쉐 1409 | 빳 1410 | 깽 1411 | 훙 1412 | 돠 1413 | 꼰 1414 | 뻘 1415 | 툴 1416 | 렐 1417 | 톤 1418 | 쥴 1419 | 💓 1420 | 첩 1421 | 뻑 1422 | 캄 1423 | 쯔 1424 | 껑 1425 | 썅 1426 | 뵙 1427 | 킄 1428 | 듕 1429 | 숲 1430 | 뎃 1431 | 쿼 1432 | 먀 1433 | 솜 1434 | 딪 1435 | 뭣 1436 | 큭 1437 | C 1438 | 슥 1439 | 풉 1440 | 쭝 1441 | 섰 1442 | 봇 1443 | 깤 1444 | 쬐 1445 | 폈 1446 | 툭 1447 | 꿇 1448 | 몹 1449 | 돕 1450 | 팽 1451 | 풋 1452 | 엮 1453 | 넥 1454 | 빚 1455 | 뎁 1456 | 쿡 1457 | 썻 1458 | 쎠 1459 | M 1460 | 팥 1461 | 쎈 1462 | 왈 1463 | D 1464 | ㆍ 1465 | n 1466 | 젛 1467 | 떄 1468 | 쏭 1469 | 룹 1470 | 읏 1471 | 읎 1472 | 둡 1473 | 샹 1474 | 쏠 1475 | 덧 1476 | 둠 1477 | 엊 1478 | 댘 1479 | 엨 1480 | 냔 1481 | 콱 1482 | 닛 1483 | 렙 1484 | 갯 1485 | 쐬 1486 | 츤 1487 | 홓 1488 | 얹 1489 | 숴 1490 | 😙 1491 | 롸 1492 | 옄 1493 | 뀜 1494 | 걀 1495 | 짴 1496 | 햤 1497 | 렜 1498 | 떳 1499 | 굵 1500 | 엏 1501 | 퐁 1502 | 췌 1503 | 뗘 1504 | 뺀 1505 | 솥 1506 | 셜 1507 | 쉅 1508 | 젝 1509 | 퍄 1510 | 횟 1511 | 쌔 1512 | 헴 1513 | 💋 1514 | 맣 1515 | 돗 1516 | 흙 1517 | 핡 1518 | 팁 1519 | 쭤 1520 | 😁 1521 | 깄 1522 | 숍 1523 | 얜 1524 | 뀔 1525 | 짭 1526 | G 1527 | 믹 1528 | 뭥 1529 | 놧 1530 | ☆ 1531 | 앟 1532 | 횡 1533 | l 1534 | 츈 1535 | 😗 1536 | 텼 1537 | 갇 1538 | 꺾 1539 | 읔 1540 | 젓 1541 | 뎀 1542 | 젼 1543 | 됭 1544 | 퀘 1545 | 헙 1546 | 웩 1547 | f 1548 | P 1549 | 홋 1550 | 떽 1551 | 챱 1552 | r 1553 | 렘 1554 | 옶 1555 | 잨 1556 | 챠 1557 | 쟨 1558 | X 1559 | 밭 1560 | O 1561 | 슐 1562 | 앖 1563 | 💜 1564 | 뱀 1565 | 삘 1566 | 탠 1567 | 얗 1568 | 🏻 1569 | 짚 1570 | 왛 1571 | 폼 1572 | 힉 1573 | 몀 1574 | 펌 1575 | 숏 1576 | 킼 1577 | 떰 1578 | 딥 1579 | 깠 1580 | 펙 1581 | 뽁 1582 | 왁 1583 | 켯 1584 | ㄳ 1585 | 괄 1586 | 뵤 1587 | 쭁 1588 | 훠 1589 | 텝 1590 | 귓 1591 | 킵 1592 | 맠 1593 | ] 1594 | 눼 1595 | 😭 1596 | 텅 1597 | 눅 1598 | 맺 1599 | 펄 1600 | 멎 1601 | u 1602 | 햏 1603 | 깈 1604 | 왘 1605 | 갛 1606 | 쓱 1607 | ☺ 1608 | 솟 1609 | 혓 1610 | 칵 1611 | 빕 1612 | 슙 1613 | h 1614 | 샥 1615 | 헥 1616 | w 1617 | 쒸 1618 | 뚠 1619 | 쨘 1620 | 녱 1621 | 깁 1622 | 츕 1623 | 젯 1624 | 듭 1625 | 뤘 1626 | 싳 1627 | [ 1628 | 훼 1629 | 뻣 1630 | 훟 1631 | 짰 1632 | 갬 1633 | 씸 1634 | 앱 1635 | 븅 1636 | ᆞ 1637 | 퀄 1638 | 웰 1639 | 칰 1640 | 먕 1641 | 셍 1642 | 😚 1643 | 맏 1644 | 넛 1645 | 꾜 1646 | 쫙 1647 | L 1648 | K 1649 | 쏴 1650 | 힙 1651 | 꿧 1652 | 킥 1653 | 뀽 1654 | V 1655 | 늉 1656 | 햅 1657 | 흣 1658 | 캣 1659 | 쥰 1660 | 앰 1661 | 촤 1662 | 웁 1663 | 잏 1664 | 뮬 1665 | 넫 1666 | 괌 1667 | 귱 1668 | ❣ 1669 | 쫀 1670 | 궄 1671 | 덴 1672 | 뎡 1673 | 꺌 1674 | 쾅 1675 | 뫄 1676 | 꼇 1677 | 쟀 1678 | 탭 1679 | ︎ 1680 | ᆢ 1681 | 샜 1682 | 딕 1683 | 돜 1684 | 싈 1685 | \ 1686 | 땄 1687 | 벳 1688 | 😆 1689 | 럈 1690 | 쉼 1691 | 멱 1692 | 멧 1693 | 밸 1694 | 뽐 1695 | 꽝 1696 | 멕 1697 | 😝 1698 | 챡 1699 | 헠 1700 | 😂 1701 | 랠 1702 | 즙 1703 | 펼 1704 | 겔 1705 | 빽 1706 | 얽 1707 | 펠 1708 | 숟 1709 | 뤼 1710 | 뺑 1711 | 톨 1712 | 잭 1713 | 갗 1714 | 뿍 1715 | 뉸 1716 | 꽥 1717 | 쯧 1718 | 볕 1719 | 빰 1720 | y 1721 | 삥 1722 | 륙 1723 | 쏜 1724 | 꾀 1725 | 뜌 1726 | 짢 1727 | 👍 1728 | R 1729 | 밬 1730 | 😋 1731 | 뢔 1732 | 띡 1733 | 겈 1734 | 쿰 1735 | 뺘 1736 | 붉 1737 | 땃 1738 | 봨 1739 | 얕 1740 | 둣 1741 | 낟 1742 | 큥 1743 | 꿉 1744 | 뚤 1745 | 뎨 1746 | 튭 1747 | 렼 1748 | 딤 1749 | 졐 1750 | 슘 1751 | 퉷 1752 | 륨 1753 | 끽 1754 | 잦 1755 | 쟌 1756 | 퐈 1757 | 젱 1758 | 윌 1759 | 쇽 1760 | 벡 1761 | 및 1762 | 쫘 1763 | 맄 1764 | 밈 1765 | 뤠 1766 | 챗 1767 | 읶 1768 | 윰 1769 | 휠 1770 | 륜 1771 | 듈 1772 | 핼 1773 | 췟 1774 | 짆 1775 | 뭡 1776 | Z 1777 | 읊 1778 | 쭘 1779 | 펫 1780 | 얶 1781 | 눴 1782 | 겝 1783 | 켁 1784 | 렄 1785 | 힠 1786 | I 1787 | 캉 1788 | 쟐 1789 | 묭 1790 | 꼐 1791 | 핮 1792 | 쩨 1793 | 뛴 1794 | 첸 1795 | • 1796 | 쌰 1797 | 😄 1798 | 팰 1799 | 🙈 1800 | 겧 1801 | 쥼 1802 | 😢 1803 | 😅 1804 | 쭐 1805 | ★ 1806 | 챔 1807 | 뛸 1808 | 멩 1809 | 퀸 1810 | 캭 1811 | 쓔 1812 | 핟 1813 | 롴 1814 | 깸 1815 | 셰 1816 | 얒 1817 | 뷴 1818 | 😳 1819 | 쯰 1820 | 겆 1821 | 룜 1822 | 놬 1823 | 쌉 1824 | 춍 1825 | 슌 1826 | 헨 1827 | 꿰 1828 | 끠 1829 | H 1830 | 웈 1831 | 맷 1832 | 쟉 1833 | 춝 1834 | 쨜 1835 | 톰 1836 | 잰 1837 | 넬 1838 | 엡 1839 | 킷 1840 | 앀 1841 | 퀵 1842 | 😏 1843 | 꿋 1844 | 댑 1845 | 큣 1846 | 똔 1847 | 됌 1848 | 큔 1849 | 💛 1850 | 씼 1851 | 🙏 1852 | 샛 1853 | 겎 1854 | 햣 1855 | 땟 1856 | 픕 1857 | 넜 1858 | 뭄 1859 | ㅝ 1860 | 💙 1861 | 찻 1862 | 켬 1863 | 앍 1864 | 젭 1865 | 춉 1866 | 즛 1867 | 틋 1868 | F 1869 | 앎 1870 | 쥥 1871 | 랖 1872 | 붐 1873 | 얐 1874 | 씐 1875 | j 1876 | U 1877 | 탆 1878 | 켈 1879 | 촵 1880 | 큽 1881 | Q 1882 | 촛 1883 | 냌 1884 | 땽 1885 | 짇 1886 | 밨 1887 | 앴 1888 | 궜 1889 | 궂 1890 | 싴 1891 | 뎄 1892 | ’ 1893 | 뭨 1894 | 껨 1895 | 읕 1896 | 읺 1897 | 빱 1898 | 쬽 1899 | 뮹 1900 | 훑 1901 | 줏 1902 | 뗄 1903 | ㅢ 1904 | 욌 1905 | E 1906 | 깟 1907 | 옇 1908 | 믕 1909 | 앆 1910 | 쁑 1911 | 꿑 1912 | 끅 1913 | 튈 1914 | 쵝 1915 | 옸 1916 | 젬 1917 | 짙 1918 | 굔 1919 | 틍 1920 | 땋 1921 | 쭙 1922 | 듸 1923 | 휙 1924 | J 1925 | 뽜 1926 | 왬 1927 | 횽 1928 | 읗 1929 | 늪 1930 | 웜 1931 | N 1932 | 옿 1933 | 냘 1934 | 룻 1935 | 뮨 1936 | 밎 1937 | 곘 1938 | 튄 1939 | 퇵 1940 | 곽 1941 | 릐 1942 | 맬 1943 | 쎙 1944 | 랃 1945 | Y 1946 | 낱 1947 | 탸 1948 | 씅 1949 | 싣 1950 | 끜 1951 | 렝 1952 | 챘 1953 | 맟 1954 | 켔 1955 | 훤 1956 | 켄 1957 | 뉭 1958 | 흨 1959 | 덯 1960 | 켤 1961 | 뷸 1962 | 쉘 1963 | 녘 1964 | 핌 1965 | 숯 1966 | 😣 1967 | 👏 1968 | 즤 1969 | 쳥 1970 | 츙 1971 | 핬 1972 | 킈 1973 | 벘 1974 | 쎵 1975 | 싷 1976 | 롷 1977 | 숱 1978 | 😐 1979 | 넋 1980 | 맸 1981 | 죤 1982 | 텄 1983 | 됔 1984 | 씰 1985 | 띰 1986 | ㅖ 1987 | 퍙 1988 | 냬 1989 | 😬 1990 | 💖 1991 | 쫒 1992 | 뺴 1993 | 궤 1994 | 켘 1995 | 툽 1996 | 뵐 1997 | 탬 1998 | 봌 1999 | 쯉 2000 | 잴 2001 | 쑈 2002 | 뭬 2003 | 캌 2004 | 슼 2005 | 믈 2006 | 둉 2007 | 컄 2008 | 쑝 2009 | 냑 2010 | 쿱 2011 | 뵹 2012 | 겋 2013 | 띤 2014 | 흩 2015 | 뉑 2016 | 됀 2017 | 😯 2018 | 휩 2019 | 꺽 2020 | 맀 2021 | 샸 2022 | 쪗 2023 | 👀 2024 | 쉑 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3357 | 덪 3358 | 싞 3359 | 벋 3360 | 댰 3361 | 켞 3362 | 봒 3363 | 섢 3364 | 쉇 3365 | 🍓 3366 | 뜐 3367 | 묨 3368 | 쓤 3369 | 랈 3370 | 퀑 3371 | 듥 3372 | 헋 3373 | 뽂 3374 | 퇼 3375 | 흏 3376 | 칯 3377 | 뇥 3378 | 먤 3379 | 맇 3380 | 욳 3381 | 껙 3382 | 룷 3383 | 뗌 3384 | 🙂 3385 | 홱 3386 | 궑 3387 | 뛃 3388 | ˊ 3389 | ˋ 3390 | 깕 3391 | 뮝 3392 | 핗 3393 | 앧 3394 | 뿩 3395 | 핚 3396 | 쩗 3397 | 흦 3398 | 끉 3399 | 삱 3400 | 옢 3401 | 윿 3402 | 🎂 3403 | ╰ 3404 | ╯ 3405 | 웧 3406 | 뙜 3407 | 깣 3408 | 넡 3409 |  3410 | 끆 3411 | 먻 3412 | 😰 3413 | 😷 3414 | 혱 3415 | 힡 3416 | 뱣 3417 | 돰 3418 | 뵴 3419 | 빪 3420 | 쭛 3421 | 둚 3422 | 픚 3423 | 팒 3424 | 뤍 3425 | 붆 3426 | 웗 3427 | 틕 3428 | 춟 3429 | 낕 3430 | 댭 3431 | 늏 3432 | 뭑 3433 | 톂 3434 | 걉 3435 | 쇳 3436 | 뫈 3437 | 굘 3438 | 🌌 3439 | 🌆 3440 | 🌃 3441 | 🍚 3442 | 💨 3443 | ☕ 3444 | 💼 3445 | 🏠 3446 | 😺 3447 | 💑 3448 | 🍣 3449 | 🌱 3450 | 🎆 3451 | 쬔 3452 | 햗 3453 | 쁰 3454 | 👩 3455 | 👧 3456 | 걧 3457 | 걳 3458 | 슾 3459 | 룅 3460 | 뢘 3461 | 헁 3462 | 쭹 3463 | 솢 3464 | 떯 3465 | 킺 3466 | ª 3467 | 핽 3468 | 줮 3469 | 큑 3470 | 죺 3471 | 믯 3472 | 탍 3473 | 道 3474 | 뻉 3475 | 덝 3476 | 돝 3477 | 냤 3478 | 쵬 3479 | 뷉 3480 | 멥 3481 | 웑 3482 | 푴 3483 | 픋 3484 | 쎋 3485 | 뻙 3486 | 틬 3487 | 뙨 3488 | 좄 3489 | 뗰 3490 | 퇑 3491 | 엷 3492 | 욦 3493 | 뼜 3494 | 쯛 3495 | 눜 3496 | 쥿 3497 | ᄊ 3498 | 쒼 3499 | 딻 3500 | 쯥 3501 | 맒 3502 | 훃 3503 | 꿬 3504 | 솬 3505 | 눳 3506 | 흝 3507 | 🧡 3508 | 칑 3509 | 츽 3510 | ã 3511 | 힋 3512 | 턿 3513 | 뺡 3514 | 뻭 3515 | 뿽 3516 | 🍻 3517 | 똗 3518 | 뛍 3519 | 뽥 3520 | 껗 3521 | 튤 3522 | ̥ 3523 | 숢 3524 | 핝 3525 | 뎝 3526 | 쌱 3527 | 갺 3528 | 뇐 3529 | 킘 3530 | 찀 3531 | 톳 3532 | 꺤 3533 | 넗 3534 | 긒 3535 | 뽭 3536 | 붴 3537 | 픁 3538 | 싚 3539 | 쌴 3540 | 굯 3541 | 헧 3542 | 둧 3543 | 홁 3544 | 윋 3545 | 칾 3546 | 햙 3547 | 횾 3548 | 쏵 3549 | 늧 3550 | 짉 3551 | 끡 3552 | 쓧 3553 | 룠 3554 | 톄 3555 | 펰 3556 | 듞 3557 | 탊 3558 | 쏌 3559 | 붤 3560 | 읈 3561 | 큡 3562 | 윝 3563 | 눰 3564 | 쬑 3565 | 칌 3566 | 쁫 3567 | 틑 3568 | 찣 3569 | 핕 3570 | 흇 3571 | 벝 3572 | 츗 3573 | 킸 3574 | 퍵 3575 | 룔 3576 | 쾀 3577 | 붝 3578 | 쾃 3579 | 먯 3580 | 뢐 3581 | 칔 3582 | 쐤 3583 | 뻛 3584 | 굄 3585 | 꼊 3586 | 쥠 3587 | 슀 3588 | 휻 3589 | 훸 3590 | 맮 3591 | 뵘 3592 | 퀙 3593 | 듟 3594 | 갌 3595 | 쑬 3596 | 쥄 3597 | 뷀 3598 | 슽 3599 | 럤 3600 | 킅 3601 | 뿥 3602 | 쳪 3603 | ○ 3604 | 心 3605 | 訪 3606 | ˚ 3607 | 풎 3608 | 뿉 3609 | 삠 3610 | 튐 3611 | 싨 3612 | さ 3613 | よ 3614 | な 3615 | ら 3616 | 읉 3617 | 렏 3618 | 켖 3619 | 🌊 3620 | 🏽 3621 | 🥂 3622 | 얟 3623 | 눦 3624 | 첰 3625 | 놁 3626 | 줖 3627 | 빆 3628 | ೭ 3629 | ੧ 3630 | ੭ 3631 | ೨ 3632 | 곜 3633 | 꿜 3634 | 뤔 3635 | 뜡 3636 | 숤 3637 | 뀹 3638 | 즂 3639 | 쎂 3640 | 덛 3641 | 잛 3642 | 💫 3643 | ⛄ 3644 | 춯 3645 | 횜 3646 | 벱 3647 | 솚 3648 | 퐅 3649 | 즵 3650 | 즴 3651 | 꼠 3652 | 뎤 3653 | 쟡 3654 | 놭 3655 | 쟷 3656 | 죈 3657 | ※ 3658 | 쎁 3659 | 뚧 3660 | 💤 3661 | 쯪 3662 | 믁 3663 | 륍 3664 | 풸 3665 | 뷕 3666 | 얅 3667 | 훪 3668 | 돳 3669 | 듓 3670 | 슠 3671 | 엝 3672 | 셓 3673 | 룀 3674 | 쩓 3675 | 쁏 3676 | 둄 3677 | 슫 3678 | 컿 3679 | 쏫 3680 | 럑 3681 | 냊 3682 | 뵫 3683 | 햰 3684 | 댹 3685 | 챼 3686 | 맊 3687 | 띳 3688 | 뗬 3689 | 풩 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3801 | 🍩 3802 | 슲 3803 | 꽠 3804 | 꽦 3805 | 꿿 3806 | 꽔 3807 | 꽧 3808 | 꽊 3809 | 챻 3810 | 깧 3811 | 욚 3812 | 켸 3813 | 섡 3814 | 🐼 3815 | 띧 3816 | 🎈 3817 | 잂 3818 | 🖐 3819 | 긧 3820 | 🍙 3821 | 횠 3822 | 띸 3823 | 쇾 3824 | 윕 3825 | 쿘 3826 | 뷩 3827 | 틩 3828 | 찲 3829 | 욫 3830 | 륌 3831 | 컺 3832 | 얓 3833 | 뤕 3834 | 봜 3835 | 욵 3836 | 🐩 3837 | 곥 3838 | 쩄 3839 | ー 3840 | 땼 3841 | 힇 3842 | 뮾 3843 | 홰 3844 | 왶 3845 | 밫 3846 | 죶 3847 | 쫔 3848 | 퐙 3849 | 냍 3850 | 쳒 3851 | 🗿 3852 | 🎥 3853 | 컅 3854 | 컃 3855 | 춸 3856 | 툲 3857 | 짒 3858 | 즎 3859 | 줓 3860 | 빷 3861 | 셇 3862 | 웇 3863 | 텂 3864 | 갳 3865 | 겓 3866 | 不 3867 | 是 3868 | 🎮 3869 | 싦 3870 | 먠 3871 | 뻇 3872 | 큱 3873 | 헜 3874 | 놥 3875 | 줚 3876 | 먛 3877 | 音 3878 | 횃 3879 | 🐤 3880 |  3881 | 쩠 3882 | 쐰 3883 | 됳 3884 | 캍 3885 | 걵 3886 | 냃 3887 | 챝 3888 | 헽 3889 | 뉩 3890 | 쀔 3891 | 떃 3892 | 》 3893 | 《 3894 | 뢯 3895 | 볩 3896 | 쾡 3897 | 굗 3898 | 벴 3899 | 걿 3900 | 닟 3901 | 웥 3902 | 쀕 3903 | 젙 3904 | ‼ 3905 | 츃 3906 | 릵 3907 | 맖 3908 | 롔 3909 | 썯 3910 | 갏 3911 | 퓩 3912 | 켭 3913 | 긹 3914 | 윜 3915 | 씋 3916 | 🔒 3917 | ₩ 3918 | 뗑 3919 | 굣 3920 | 👌 3921 | 뿢 3922 | 븃 3923 | 쥨 3924 | 앾 3925 | ♧ 3926 | 🤐 3927 | 쌬 3928 | 😓 3929 | 룤 3930 | 🙅 3931 | ♂ 3932 | 븙 3933 | 졑 3934 | 돍 3935 | 녾 3936 | 짵 3937 | 쒭 3938 | 쏩 3939 | 👻 3940 | 칿 3941 | 뛓 3942 | 떘 3943 | 츌 3944 | 볽 3945 | 삡 3946 | 🌝 3947 | 얆 3948 | 읷 3949 | 릍 3950 | 틹 3951 | 띙 3952 | 럸 3953 | 좈 3954 | 홤 3955 | 븢 3956 | 몿 3957 | 뱜 3958 | 뮁 3959 | 덖 3960 | 턉 3961 | 첧 3962 | 껼 3963 | 쿧 3964 |   3965 | 🍑 3966 | 셪 3967 | 벊 3968 | 쨧 3969 | 댶 3970 | 왙 3971 | 헾 3972 | 뮜 3973 | 뮵 3974 | 귴 3975 | 멯 3976 | 읳 3977 | 쎃 3978 | 흒 3979 | 랲 3980 | 뷍 3981 | 챟 3982 | 딯 3983 | 꺔 3984 | 뽏 3985 | 랋 3986 | 짺 3987 | 솹 3988 | 묙 3989 | 냒 3990 | 쁙 3991 | ！ 3992 | 괒 3993 | 솼 3994 | 듡 3995 | 쨓 3996 | 슏 3997 | 쩰 3998 | 붚 3999 | 럊 4000 | 됝 4001 | 🐴 4002 | 뵏 4003 | 횔 4004 | 숵 4005 | 뭗 4006 | 쫴 4007 | 샪 4008 | 섮 4009 | 💌 4010 | 놏 4011 | 넏 4012 | 둦 4013 | 팹 4014 | 쮄 4015 | 툌 4016 | 뉠 4017 | 촨 4018 | 쐄 4019 | 쐥 4020 | 쫜 4021 | 뱽 4022 | 뎹 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4134 | 됍 4135 | 쓉 4136 | 딖 4137 | 댚 4138 | 럮 4139 | 쩛 4140 | 엃 4141 | 췩 4142 | 랅 4143 | 밮 4144 | 펃 4145 | 앿 4146 | 퀜 4147 | 뙷 4148 | 푤 4149 | 옧 4150 | 팦 4151 | 킫 4152 | 늰 4153 | 퓍 4154 | 🍯 4155 | 븀 4156 | 엜 4157 | 턍 4158 | 쏮 4159 | 죚 4160 | 뒇 4161 | 뺫 4162 | 쿳 4163 | 뻫 4164 | 튽 4165 | 흓 4166 | 홎 4167 | 촏 4168 | 굻 4169 | 쭏 4170 | 곷 4171 | 덍 4172 | 궆 4173 | 꺋 4174 | □ 4175 | 짃 4176 | 뒫 4177 | 뤗 4178 | 빂 4179 | 츳 4180 | 렅 4181 | 훝 4182 | 젋 4183 | 떅 4184 | 혇 4185 | 탛 4186 | 밳 4187 | 낹 4188 | 붅 4189 | { 4190 | 렢 4191 | 왅 4192 | 뀟 4193 | 짶 4194 | 쓿 4195 | 슧 4196 | 롲 4197 | 쳑 4198 | 곙 4199 | 죴 4200 | 쾩 4201 | 큄 4202 | 꺟 4203 | 🧐 4204 | 켙 4205 | ↑ 4206 | 쁟 4207 | 뭀 4208 | 몝 4209 | 쳬 4210 | 솽 4211 | 쓍 4212 | 덦 4213 | 😦 4214 | 듖 4215 | 꼮 4216 | 맫 4217 | 곃 4218 | 굇 4219 | 얩 4220 | 还 4221 | 没 4222 | 终 4223 | で 4224 | き 4225 | る 4226 | 子 4227 | 多 4228 | 女 4229 | 듑 4230 | 🎎 4231 | 틘 4232 | 폘 4233 | 쟸 4234 | 륃 4235 | 넁 4236 | 펗 4237 | 뱈 4238 | 꽜 4239 | 롘 4240 | 찴 4241 | 솃 4242 | 윧 4243 | 폨 4244 | ● 4245 | ٩ 4246 | ۶ 4247 | 럳 4248 | 곦 4249 | 꾺 4250 | 쿻 4251 | -------------------------------------------------------------------------------- /quickspacer/resources/default_saved_model/2/saved_model.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmoquester/quickspacer/f7292ee42227d68c7432fd929360f1d6664124bf/quickspacer/resources/default_saved_model/2/saved_model.pb -------------------------------------------------------------------------------- /quickspacer/resources/default_saved_model/2/variables/variables.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmoquester/quickspacer/f7292ee42227d68c7432fd929360f1d6664124bf/quickspacer/resources/default_saved_model/2/variables/variables.data-00000-of-00001 -------------------------------------------------------------------------------- /quickspacer/resources/default_saved_model/2/variables/variables.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmoquester/quickspacer/f7292ee42227d68c7432fd929360f1d6664124bf/quickspacer/resources/default_saved_model/2/variables/variables.index -------------------------------------------------------------------------------- /quickspacer/resources/default_saved_model/3/assets/vocab.txt: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ㅋ 5 | 이 6 | 아 7 | 어 8 | 고 9 | 가 10 | 그 11 | 다 12 | 지 13 | 나 14 | 는 15 | 하 16 | ? 17 | 도 18 | 서 19 | . 20 | 요 21 | 기 22 | 야 23 | 해 24 | 자 25 | 에 26 | 니 27 | 데 28 | 게 29 | 안 30 | 거 31 | 오 32 | 면 33 | 리 34 | 은 35 | 내 36 | 라 37 | 있 38 | 사 39 | 시 40 | 래 41 | 보 42 | 일 43 | 만 44 | 까 45 | 네 46 | 한 47 | 으 48 | 구 49 | 로 50 | 을 51 | 좋 52 | 너 53 | 먹 54 | 제 55 | 마 56 | 랑 57 | 겠 58 | 들 59 | 무 60 | 여 61 | 말 62 | 응 63 | 진 64 | 었 65 | 주 66 | 했 67 | 우 68 | ㅎ 69 | 러 70 | ! 71 | 려 72 | 수 73 | 뭐 74 | 잘 75 | 대 76 | 인 77 | 금 78 | 정 79 | 같 80 | ㅠ 81 | 부 82 | 없 83 | 음 84 | 왜 85 | 더 86 | 알 87 | 저 88 | 생 89 | 근 90 | 난 91 | 짜 92 | 할 93 | 미 94 | 워 95 | 늘 96 | 전 97 | 히 98 | 모 99 | 바 100 | 럼 101 | 것 102 | 스 103 | 많 104 | 간 105 | 건 106 | 애 107 | 상 108 | 싶 109 | 웅 110 | 와 111 | 렇 112 | 빠 113 | 좀 114 | 귀 115 | 되 116 | 예 117 | 집 118 | 심 119 | 냥 120 | 냐 121 | 못 122 | ~ 123 | 때 124 | 았 125 | 화 126 | 치 127 | 를 128 | 쪽 129 | , 130 | 런 131 | 장 132 | 르 133 | 중 134 | 연 135 | 비 136 | 의 137 | 봐 138 | 맞 139 | 각 140 | 신 141 | 돼 142 | 당 143 | 조 144 | 소 145 | 분 146 | 원 147 | ㅜ 148 | 찮 149 | 디 150 | 공 151 | 람 152 | 피 153 | 잖 154 | 두 155 | 괜 156 | 언 157 | 날 158 | 실 159 | 테 160 | 뽀 161 | 세 162 | 배 163 | 동 164 | 직 165 | 않 166 | 걸 167 | 1 168 | 친 169 | 운 170 | 누 171 | 방 172 | 왔 173 | 갈 174 | 얼 175 | 번 176 | 남 177 | 엄 178 | 님 179 | 줄 180 | 단 181 | 학 182 | 잠 183 | 드 184 | 밥 185 | 행 186 | 물 187 | 형 188 | 끝 189 | 든 190 | 울 191 | 른 192 | 터 193 | 속 194 | 재 195 | 머 196 | 용 197 | 입 198 | 줘 199 | 헤 200 | 문 201 | 트 202 | 약 203 | 영 204 | 져 205 | 청 206 | 발 207 | 교 208 | 버 209 | 살 210 | 개 211 | 경 212 | 힘 213 | 올 214 | 민 215 | 계 216 | 노 217 | 싫 218 | 쁘 219 | 또 220 | 몰 221 | 따 222 | 맛 223 | 긴 224 | 적 225 | 차 226 | 유 227 | 파 228 | 군 229 | 졸 230 | 감 231 | 엉 232 | 프 233 | 선 234 | 불 235 | 놀 236 | 찍 237 | 2 238 | 준 239 | 빨 240 | 타 241 | 혼 242 | 던 243 | 호 244 | 받 245 | 성 246 | 0 247 | 났 248 | ㅇ 249 | 과 250 | 달 251 | 착 252 | 허 253 | 후 254 | 임 255 | 열 256 | 늦 257 | 겨 258 | 국 259 | 느 260 | 반 261 | 막 262 | 점 263 | 처 264 | 볼 265 | 카 266 | 곤 267 | 씻 268 | 락 269 | 죠 270 | 매 271 | 랬 272 | 쓰 273 | 식 274 | 강 275 | 얘 276 | 갔 277 | 토 278 | 쉬 279 | 뻐 280 | 써 281 | 침 282 | 새 283 | 크 284 | 눈 285 | 레 286 | 뭔 287 | 키 288 | 끼 289 | 위 290 | 질 291 | 회 292 | 길 293 | 슨 294 | 글 295 | 떻 296 | 엽 297 | 목 298 | 앙 299 | 통 300 | 천 301 | 추 302 | 함 303 | 봤 304 | 편 305 | 출 306 | 3 307 | 죽 308 | 현 309 | 역 310 | 헐 311 | 별 312 | 닌 313 | 씨 314 | 관 315 | 업 316 | 양 317 | 루 318 | 명 319 | 망 320 | 꾸 321 | 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필 447 | 확 448 | 겟 449 | 될 450 | 텐 451 | 월 452 | 밤 453 | 변 454 | 둘 455 | 란 456 | 놓 457 | 꼬 458 | 즘 459 | 혜 460 | 흐 461 | 몇 462 | 합 463 | 종 464 | 똑 465 | 옆 466 | 플 467 | 엇 468 | 등 469 | 움 470 | 채 471 | 책 472 | 욕 473 | 뎅 474 | 풀 475 | 줬 476 | 페 477 | 겼 478 | 투 479 | 곧 480 | 6 481 | 랐 482 | 격 483 | 쳤 484 | 얌 485 | 멀 486 | 혀 487 | 닝 488 | 윤 489 | 뒤 490 | 론 491 | 족 492 | 떤 493 | 급 494 | 솔 495 | 흥 496 | 쟈 497 | 삼 498 | 뜻 499 | 낌 500 | 승 501 | 맘 502 | 섭 503 | 암 504 | 7 505 | 넣 506 | 빼 507 | 9 508 | 걔 509 | 8 510 | 쩌 511 | 팅 512 | 밀 513 | 쯤 514 | ㄷ 515 | 푹 516 | 떨 517 | 팔 518 | 떠 519 | 석 520 | 희 521 | 믿 522 | 철 523 | 숙 524 | 규 525 | 퍼 526 | 왕 527 | 빈 528 | 킨 529 | 쿠 530 | 멍 531 | 햇 532 | 붙 533 | 곳 534 | 읽 535 | 걍 536 | 켜 537 | 창 538 | 찬 539 | 쪼 540 | 악 541 | 딩 542 | 욤 543 | 빵 544 | 틀 545 | 픈 546 | 헉 547 | 큰 548 | 송 549 | 향 550 | 앉 551 | 짓 552 | 즈 553 | 닥 554 | 료 555 | 황 556 | 록 557 | 흑 558 | 힝 559 | 섹 560 | 효 561 | 벽 562 | 십 563 | 법 564 | 혹 565 | 택 566 | 옴 567 | 끊 568 | 끔 569 | 판 570 | 뿌 571 | 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쥐 947 | 얍 948 | 칫 949 | 깍 950 | ㅔ 951 | 앵 952 | 늙 953 | 틈 954 | 헬 955 | 혐 956 | 핳 957 | 엘 958 | 싼 959 | 넓 960 | 촉 961 | s 962 | 뚜 963 | 컵 964 | 셀 965 | 녔 966 | 닠 967 | 땠 968 | 뜯 969 | 폐 970 | 땅 971 | 밋 972 | 땀 973 | 햐 974 | 좌 975 | 협 976 | 굉 977 | 끓 978 | 튜 979 | 뜩 980 | 넴 981 | 툰 982 | 삶 983 | 뮤 984 | 힣 985 | 셋 986 | 잣 987 | 흡 988 | 녜 989 | 왤 990 | 꼼 991 | 봣 992 | 쌓 993 | > 994 | 헝 995 | 율 996 | 뺏 997 | 룩 998 | 엎 999 | 겜 1000 | 낳 1001 | 닼 1002 | 꼴 1003 | 칙 1004 | 뻤 1005 | 릇 1006 | 껏 1007 | 뒀 1008 | 벨 1009 | 훗 1010 | 쩍 1011 | 굶 1012 | 낄 1013 | 쾌 1014 | 휘 1015 | 랍 1016 | 헹 1017 | 닙 1018 | 뎈 1019 | 뜬 1020 | 룰 1021 | 슝 1022 | 웹 1023 | 혈 1024 | 넼 1025 | 픔 1026 | 쌈 1027 | 쳇 1028 | 탑 1029 | 흰 1030 | 뒈 1031 | 홈 1032 | 됬 1033 | 긁 1034 | 쵸 1035 | 앤 1036 | 훔 1037 | 묘 1038 | 셧 1039 | 뽕 1040 | 짘 1041 | 뇌 1042 | < 1043 | 둔 1044 | 객 1045 | 췄 1046 | 컸 1047 | ㅊ 1048 | 컷 1049 | 땜 1050 | 끙 1051 | 듀 1052 | ㅆ 1053 | 긔 1054 | 샐 1055 | 눕 1056 | 뱃 1057 | 엠 1058 | 죵 1059 | 톱 1060 | 꽉 1061 | 놉 1062 | 겐 1063 | 홉 1064 | 왼 1065 | 얀 1066 | 썩 1067 | 쌩 1068 | 먄 1069 | 찝 1070 | 콤 1071 | 긋 1072 | 틴 1073 | 졋 1074 | 헌 1075 | 줭 1076 | 률 1077 | 곸 1078 | 힛 1079 | 좁 1080 | 넝 1081 | 뎌 1082 | 뭉 1083 | 낀 1084 | 샵 1085 | 랰 1086 | 훌 1087 | 썸 1088 | 싹 1089 | 펴 1090 | 멜 1091 | @ 1092 | ㅃ 1093 | 꽂 1094 | 짬 1095 | 찢 1096 | 껭 1097 | 꽁 1098 | 뿡 1099 | 벤 1100 | 윗 1101 | 봅 1102 | 킁 1103 | 닿 1104 | 얄 1105 | ( 1106 | 뷔 1107 | 웟 1108 | 닷 1109 | 컬 1110 | 밴 1111 | # 1112 | 굼 1113 | 떴 1114 | 눠 1115 | 촬 1116 | 얇 1117 | 귄 1118 | 닉 1119 | 핵 1120 | 웨 1121 | 셩 1122 | 껐 1123 | ㅛ 1124 | 됩 1125 | 썹 1126 | 겄 1127 | 쮸 1128 | ㅍ 1129 | 봥 1130 | 펜 1131 | 펑 1132 | 덤 1133 | ㅑ 1134 | 렛 1135 | 틱 1136 | 뚫 1137 | 픽 1138 | 😘 1139 | 냅 1140 | 팝 1141 | 뺨 1142 | 퀴 1143 | 값 1144 | 쟝 1145 | 😍 1146 | 턱 1147 | 겉 1148 | 띵 1149 | c 1150 | 즁 1151 | 밉 1152 | 젊 1153 | 퉁 1154 | 셉 1155 | m 1156 | 섬 1157 | 쫓 1158 | 쩜 1159 | 눙 1160 | 숫 1161 | 뱅 1162 | 랏 1163 | 숑 1164 | 깻 1165 | 샷 1166 | / 1167 | 델 1168 | % 1169 | 콧 1170 | 햌 1171 | * 1172 | 깎 1173 | a 1174 | 뼈 1175 | 뻗 1176 | 팍 1177 | 쬬 1178 | 쉿 1179 | 램 1180 | 됏 1181 | = 1182 | 덟 1183 | 😉 1184 | 샌 1185 | 싯 1186 | 겻 1187 | 빗 1188 | 꿍 1189 | 핥 1190 | 텨 1191 | 댄 1192 | 쏟 1193 | 빔 1194 | 갱 1195 | 캠 1196 | 뜸 1197 | 볍 1198 | 템 1199 | 텀 1200 | 뤄 1201 | 랙 1202 | " 1203 | 홧 1204 | 엿 1205 | 돋 1206 | 킴 1207 | 낯 1208 | 략 1209 | 핀 1210 | 렉 1211 | 깬 1212 | 괘 1213 | 떵 1214 | 맑 1215 | 껀 1216 | 💗 1217 | 꾹 1218 | 뱉 1219 | 옳 1220 | 컹 1221 | ㄸ 1222 | 붓 1223 | 읭 1224 | 맴 1225 | 낑 1226 | 갤 1227 | 섞 1228 | 삿 1229 | 쪘 1230 | 뒹 1231 | 캔 1232 | 묜 1233 | 껍 1234 | 랩 1235 | + 1236 | 깃 1237 | 굽 1238 | 밟 1239 | 쬭 1240 | 욬 1241 | 렬 1242 | 띄 1243 | 튕 1244 | 컥 1245 | 룽 1246 | ㅉ 1247 | p 1248 | 낰 1249 | 둑 1250 | 앨 1251 | 귤 1252 | 넉 1253 | 욘 1254 | o 1255 | t 1256 | 칸 1257 | 팟 1258 | 엣 1259 | 웤 1260 | 씌 1261 | 쫍 1262 | 빅 1263 | 낚 1264 | 맵 1265 | 뇸 1266 | 틸 1267 | 줍 1268 | 랗 1269 | 넨 1270 | 븐 1271 | 욧 1272 | 팸 1273 | 흉 1274 | 쇠 1275 | A 1276 | 즉 1277 | 릅 1278 | 럿 1279 | v 1280 | 텍 1281 | 뢰 1282 | 곁 1283 | T 1284 | 쉰 1285 | 측 1286 | 돔 1287 | 썰 1288 | 뜽 1289 | 룡 1290 | 쩝 1291 | 랔 1292 | 띨 1293 | 씽 1294 | 꺅 1295 | 뺐 1296 | i 1297 | 멸 1298 | 댐 1299 | 됫 1300 | 륭 1301 | 핰 1302 | 쳣 1303 | 섴 1304 | 퉤 1305 | 긱 1306 | 왴 1307 | 냨 1308 | 똘 1309 | 뿔 1310 | 줫 1311 | 뭇 1312 | 꼽 1313 | 훅 1314 | 밧 1315 | 퍽 1316 | d 1317 | 헣 1318 | b 1319 | 잎 1320 | e 1321 | 댱 1322 | 뉘 1323 | 됑 1324 | 😊 1325 | 꽈 1326 | 콕 1327 | ㅕ 1328 | 룬 1329 | 벙 1330 | 뺄 1331 | 뜰 1332 | 짖 1333 | 쀼 1334 | 됴 1335 | 댈 1336 | 숩 1337 | 갘 1338 | 멤 1339 | 겪 1340 | 옼 1341 | 걘 1342 | B 1343 | 곀 1344 | 궈 1345 | 푼 1346 | S 1347 | 쏙 1348 | 쌌 1349 | 벚 1350 | 늑 1351 | 핏 1352 | 즌 1353 | 갚 1354 | 냇 1355 | 롯 1356 | g 1357 | 딧 1358 | 펐 1359 | 쑥 1360 | 삽 1361 | 얏 1362 | 팜 1363 | 귈 1364 | 깰 1365 | 뿜 1366 | 슉 1367 | 칩 1368 | 펀 1369 | 늠 1370 | 셤 1371 | 뱌 1372 | 찼 1373 | 윙 1374 | 쨩 1375 | k 1376 | 쫑 1377 | 찹 1378 | 뾰 1379 | 땨 1380 | 겤 1381 | 😌 1382 | 껌 1383 | 갰 1384 | 닳 1385 | 폴 1386 | … 1387 | 쇄 1388 | 몫 1389 | 앓 1390 | 쨋 1391 | 쨍 1392 | 썬 1393 | 놋 1394 | 씁 1395 | 늬 1396 | x 1397 | 흫 1398 | 뀐 1399 | 댁 1400 | 듦 1401 | z 1402 | 깥 1403 | 옙 1404 | 💞 1405 | 뵈 1406 | 샴 1407 | 펭 1408 | 쉐 1409 | 빳 1410 | 깽 1411 | 훙 1412 | 돠 1413 | 꼰 1414 | 뻘 1415 | 툴 1416 | 렐 1417 | 톤 1418 | 쥴 1419 | 💓 1420 | 첩 1421 | 뻑 1422 | 캄 1423 | 쯔 1424 | 껑 1425 | 썅 1426 | 뵙 1427 | 킄 1428 | 듕 1429 | 숲 1430 | 뎃 1431 | 쿼 1432 | 먀 1433 | 솜 1434 | 딪 1435 | 뭣 1436 | 큭 1437 | C 1438 | 슥 1439 | 풉 1440 | 쭝 1441 | 섰 1442 | 봇 1443 | 깤 1444 | 쬐 1445 | 폈 1446 | 툭 1447 | 꿇 1448 | 몹 1449 | 돕 1450 | 팽 1451 | 풋 1452 | 엮 1453 | 넥 1454 | 빚 1455 | 뎁 1456 | 쿡 1457 | 썻 1458 | 쎠 1459 | M 1460 | 팥 1461 | 쎈 1462 | 왈 1463 | D 1464 | ㆍ 1465 | n 1466 | 젛 1467 | 떄 1468 | 쏭 1469 | 룹 1470 | 읏 1471 | 읎 1472 | 둡 1473 | 샹 1474 | 쏠 1475 | 덧 1476 | 둠 1477 | 엊 1478 | 댘 1479 | 엨 1480 | 냔 1481 | 콱 1482 | 닛 1483 | 렙 1484 | 갯 1485 | 쐬 1486 | 츤 1487 | 홓 1488 | 얹 1489 | 숴 1490 | 😙 1491 | 롸 1492 | 옄 1493 | 뀜 1494 | 걀 1495 | 짴 1496 | 햤 1497 | 렜 1498 | 떳 1499 | 굵 1500 | 엏 1501 | 퐁 1502 | 췌 1503 | 뗘 1504 | 뺀 1505 | 솥 1506 | 셜 1507 | 쉅 1508 | 젝 1509 | 퍄 1510 | 횟 1511 | 쌔 1512 | 헴 1513 | 💋 1514 | 맣 1515 | 돗 1516 | 흙 1517 | 핡 1518 | 팁 1519 | 쭤 1520 | 😁 1521 | 깄 1522 | 숍 1523 | 얜 1524 | 뀔 1525 | 짭 1526 | G 1527 | 믹 1528 | 뭥 1529 | 놧 1530 | ☆ 1531 | 앟 1532 | 횡 1533 | l 1534 | 츈 1535 | 😗 1536 | 텼 1537 | 갇 1538 | 꺾 1539 | 읔 1540 | 젓 1541 | 뎀 1542 | 젼 1543 | 됭 1544 | 퀘 1545 | 헙 1546 | 웩 1547 | f 1548 | P 1549 | 홋 1550 | 떽 1551 | 챱 1552 | r 1553 | 렘 1554 | 옶 1555 | 잨 1556 | 챠 1557 | 쟨 1558 | X 1559 | 밭 1560 | O 1561 | 슐 1562 | 앖 1563 | 💜 1564 | 뱀 1565 | 삘 1566 | 탠 1567 | 얗 1568 | 🏻 1569 | 짚 1570 | 왛 1571 | 폼 1572 | 힉 1573 | 몀 1574 | 펌 1575 | 숏 1576 | 킼 1577 | 떰 1578 | 딥 1579 | 깠 1580 | 펙 1581 | 뽁 1582 | 왁 1583 | 켯 1584 | ㄳ 1585 | 괄 1586 | 뵤 1587 | 쭁 1588 | 훠 1589 | 텝 1590 | 귓 1591 | 킵 1592 | 맠 1593 | ] 1594 | 눼 1595 | 😭 1596 | 텅 1597 | 눅 1598 | 맺 1599 | 펄 1600 | 멎 1601 | u 1602 | 햏 1603 | 깈 1604 | 왘 1605 | 갛 1606 | 쓱 1607 | ☺ 1608 | 솟 1609 | 혓 1610 | 칵 1611 | 빕 1612 | 슙 1613 | h 1614 | 샥 1615 | 헥 1616 | w 1617 | 쒸 1618 | 뚠 1619 | 쨘 1620 | 녱 1621 | 깁 1622 | 츕 1623 | 젯 1624 | 듭 1625 | 뤘 1626 | 싳 1627 | [ 1628 | 훼 1629 | 뻣 1630 | 훟 1631 | 짰 1632 | 갬 1633 | 씸 1634 | 앱 1635 | 븅 1636 | ᆞ 1637 | 퀄 1638 | 웰 1639 | 칰 1640 | 먕 1641 | 셍 1642 | 😚 1643 | 맏 1644 | 넛 1645 | 꾜 1646 | 쫙 1647 | L 1648 | K 1649 | 쏴 1650 | 힙 1651 | 꿧 1652 | 킥 1653 | 뀽 1654 | V 1655 | 늉 1656 | 햅 1657 | 흣 1658 | 캣 1659 | 쥰 1660 | 앰 1661 | 촤 1662 | 웁 1663 | 잏 1664 | 뮬 1665 | 넫 1666 | 괌 1667 | 귱 1668 | ❣ 1669 | 쫀 1670 | 궄 1671 | 덴 1672 | 뎡 1673 | 꺌 1674 | 쾅 1675 | 뫄 1676 | 꼇 1677 | 쟀 1678 | 탭 1679 | ︎ 1680 | ᆢ 1681 | 샜 1682 | 딕 1683 | 돜 1684 | 싈 1685 | \ 1686 | 땄 1687 | 벳 1688 | 😆 1689 | 럈 1690 | 쉼 1691 | 멱 1692 | 멧 1693 | 밸 1694 | 뽐 1695 | 꽝 1696 | 멕 1697 | 😝 1698 | 챡 1699 | 헠 1700 | 😂 1701 | 랠 1702 | 즙 1703 | 펼 1704 | 겔 1705 | 빽 1706 | 얽 1707 | 펠 1708 | 숟 1709 | 뤼 1710 | 뺑 1711 | 톨 1712 | 잭 1713 | 갗 1714 | 뿍 1715 | 뉸 1716 | 꽥 1717 | 쯧 1718 | 볕 1719 | 빰 1720 | y 1721 | 삥 1722 | 륙 1723 | 쏜 1724 | 꾀 1725 | 뜌 1726 | 짢 1727 | 👍 1728 | R 1729 | 밬 1730 | 😋 1731 | 뢔 1732 | 띡 1733 | 겈 1734 | 쿰 1735 | 뺘 1736 | 붉 1737 | 땃 1738 | 봨 1739 | 얕 1740 | 둣 1741 | 낟 1742 | 큥 1743 | 꿉 1744 | 뚤 1745 | 뎨 1746 | 튭 1747 | 렼 1748 | 딤 1749 | 졐 1750 | 슘 1751 | 퉷 1752 | 륨 1753 | 끽 1754 | 잦 1755 | 쟌 1756 | 퐈 1757 | 젱 1758 | 윌 1759 | 쇽 1760 | 벡 1761 | 및 1762 | 쫘 1763 | 맄 1764 | 밈 1765 | 뤠 1766 | 챗 1767 | 읶 1768 | 윰 1769 | 휠 1770 | 륜 1771 | 듈 1772 | 핼 1773 | 췟 1774 | 짆 1775 | 뭡 1776 | Z 1777 | 읊 1778 | 쭘 1779 | 펫 1780 | 얶 1781 | 눴 1782 | 겝 1783 | 켁 1784 | 렄 1785 | 힠 1786 | I 1787 | 캉 1788 | 쟐 1789 | 묭 1790 | 꼐 1791 | 핮 1792 | 쩨 1793 | 뛴 1794 | 첸 1795 | • 1796 | 쌰 1797 | 😄 1798 | 팰 1799 | 🙈 1800 | 겧 1801 | 쥼 1802 | 😢 1803 | 😅 1804 | 쭐 1805 | ★ 1806 | 챔 1807 | 뛸 1808 | 멩 1809 | 퀸 1810 | 캭 1811 | 쓔 1812 | 핟 1813 | 롴 1814 | 깸 1815 | 셰 1816 | 얒 1817 | 뷴 1818 | 😳 1819 | 쯰 1820 | 겆 1821 | 룜 1822 | 놬 1823 | 쌉 1824 | 춍 1825 | 슌 1826 | 헨 1827 | 꿰 1828 | 끠 1829 | H 1830 | 웈 1831 | 맷 1832 | 쟉 1833 | 춝 1834 | 쨜 1835 | 톰 1836 | 잰 1837 | 넬 1838 | 엡 1839 | 킷 1840 | 앀 1841 | 퀵 1842 | 😏 1843 | 꿋 1844 | 댑 1845 | 큣 1846 | 똔 1847 | 됌 1848 | 큔 1849 | 💛 1850 | 씼 1851 | 🙏 1852 | 샛 1853 | 겎 1854 | 햣 1855 | 땟 1856 | 픕 1857 | 넜 1858 | 뭄 1859 | ㅝ 1860 | 💙 1861 | 찻 1862 | 켬 1863 | 앍 1864 | 젭 1865 | 춉 1866 | 즛 1867 | 틋 1868 | F 1869 | 앎 1870 | 쥥 1871 | 랖 1872 | 붐 1873 | 얐 1874 | 씐 1875 | j 1876 | U 1877 | 탆 1878 | 켈 1879 | 촵 1880 | 큽 1881 | Q 1882 | 촛 1883 | 냌 1884 | 땽 1885 | 짇 1886 | 밨 1887 | 앴 1888 | 궜 1889 | 궂 1890 | 싴 1891 | 뎄 1892 | ’ 1893 | 뭨 1894 | 껨 1895 | 읕 1896 | 읺 1897 | 빱 1898 | 쬽 1899 | 뮹 1900 | 훑 1901 | 줏 1902 | 뗄 1903 | ㅢ 1904 | 욌 1905 | E 1906 | 깟 1907 | 옇 1908 | 믕 1909 | 앆 1910 | 쁑 1911 | 꿑 1912 | 끅 1913 | 튈 1914 | 쵝 1915 | 옸 1916 | 젬 1917 | 짙 1918 | 굔 1919 | 틍 1920 | 땋 1921 | 쭙 1922 | 듸 1923 | 휙 1924 | J 1925 | 뽜 1926 | 왬 1927 | 횽 1928 | 읗 1929 | 늪 1930 | 웜 1931 | N 1932 | 옿 1933 | 냘 1934 | 룻 1935 | 뮨 1936 | 밎 1937 | 곘 1938 | 튄 1939 | 퇵 1940 | 곽 1941 | 릐 1942 | 맬 1943 | 쎙 1944 | 랃 1945 | Y 1946 | 낱 1947 | 탸 1948 | 씅 1949 | 싣 1950 | 끜 1951 | 렝 1952 | 챘 1953 | 맟 1954 | 켔 1955 | 훤 1956 | 켄 1957 | 뉭 1958 | 흨 1959 | 덯 1960 | 켤 1961 | 뷸 1962 | 쉘 1963 | 녘 1964 | 핌 1965 | 숯 1966 | 😣 1967 | 👏 1968 | 즤 1969 | 쳥 1970 | 츙 1971 | 핬 1972 | 킈 1973 | 벘 1974 | 쎵 1975 | 싷 1976 | 롷 1977 | 숱 1978 | 😐 1979 | 넋 1980 | 맸 1981 | 죤 1982 | 텄 1983 | 됔 1984 | 씰 1985 | 띰 1986 | ㅖ 1987 | 퍙 1988 | 냬 1989 | 😬 1990 | 💖 1991 | 쫒 1992 | 뺴 1993 | 궤 1994 | 켘 1995 | 툽 1996 | 뵐 1997 | 탬 1998 | 봌 1999 | 쯉 2000 | 잴 2001 | 쑈 2002 | 뭬 2003 | 캌 2004 | 슼 2005 | 믈 2006 | 둉 2007 | 컄 2008 | 쑝 2009 | 냑 2010 | 쿱 2011 | 뵹 2012 | 겋 2013 | 띤 2014 | 흩 2015 | 뉑 2016 | 됀 2017 | 😯 2018 | 휩 2019 | 꺽 2020 | 맀 2021 | 샸 2022 | 쪗 2023 | 👀 2024 | 쉑 2025 | 쌋 2026 | 죻 2027 | 눔 2028 | 칞 2029 | 팤 2030 | 렠 2031 | 붸 2032 | 뷥 2033 | 셌 2034 | 짹 2035 | 샄 2036 | 놰 2037 | 🐕 2038 | ☞ 2039 | ☜ 2040 | 섣 2041 | & 2042 | 쨀 2043 | 윳 2044 | 돟 2045 | 😀 2046 | 몈 2047 | 줰 2048 | 굠 2049 | 켠 2050 | 늗 2051 | 첵 2052 | 😥 2053 | 뮈 2054 | 똬 2055 | 쪈 2056 | 촥 2057 | 걌 2058 | 앚 2059 | 쎅 2060 | 펩 2061 | 뗭 2062 | 쌕 2063 | 뱔 2064 | 혬 2065 | 촙 2066 | 쥔 2067 | 툼 2068 | 뵨 2069 | 앳 2070 | 볐 2071 | 먿 2072 | 먾 2073 | 뚬 2074 | 셥 2075 | 봬 2076 | 싀 2077 | 켕 2078 | 쉣 2079 | 컁 2080 | 짛 2081 | 낵 2082 | 닜 2083 | 삑 2084 | 뗐 2085 | 봊 2086 | 잫 2087 | 뚀 2088 | 🤔 2089 | 곺 2090 | 챌 2091 | 뺌 2092 | 쟜 2093 | 믐 2094 | 쥭 2095 | 싰 2096 | 꺙 2097 | 닪 2098 | 뽄 2099 | 😞 2100 | 꿩 2101 | 왐 2102 | 큘 2103 | 갹 2104 | 펏 2105 | 쨰 2106 | 뼛 2107 | 잽 2108 | 휑 2109 | 욀 2110 | 쒀 2111 | 걐 2112 | 😛 2113 | 갼 2114 | 챕 2115 | 솨 2116 | 댠 2117 | 덱 2118 | 핍 2119 | 뉨 2120 | 뀰 2121 | ㅘ 2122 | 읅 2123 | 럄 2124 | 삔 2125 | 쎀 2126 | 캇 2127 | 둿 2128 | 펨 2129 | 퐝 2130 | 닊 2131 | 쿜 2132 | 뉵 2133 | 뭠 2134 | 읻 2135 | 🙆 2136 | 왭 2137 | 윅 2138 | 꼿 2139 | ㅙ 2140 | 멓 2141 | 🐶 2142 | 졈 2143 | 쟛 2144 | 탤 2145 | 톧 2146 | 옜 2147 | 믾 2148 | 휭 2149 | 갴 2150 | 쬠 2151 | 싄 2152 | 갭 2153 | 뾱 2154 | 옝 2155 | 쟘 2156 | 춌 2157 | 떫 2158 | 앝 2159 | 쁩 2160 | 찯 2161 | 찧 2162 | 옘 2163 | 맢 2164 | 쥽 2165 | 휜 2166 | 컾 2167 | 퓌 2168 | 헵 2169 | 긑 2170 | 챈 2171 | 냣 2172 | 왑 2173 | 섀 2174 | 폿 2175 | 콸 2176 | 귐 2177 | 릏 2178 | 푠 2179 | 끛 2180 | ‍ 2181 | 놩 2182 | 쿸 2183 | 쿙 2184 | 믓 2185 | 귯 2186 | 빸 2187 | 쟙 2188 | 횬 2189 | 춧 2190 | 잒 2191 | 힁 2192 | 삣 2193 | 뷱 2194 | 렣 2195 | 닏 2196 | 짯 2197 | 츰 2198 | 깉 2199 | 끵 2200 | 좊 2201 | 빻 2202 | 좃 2203 | 녓 2204 | 쐈 2205 | 햬 2206 | 젆 2207 | 귿 2208 | 캎 2209 | 😔 2210 | 캥 2211 | 똫 2212 | 텁 2213 | 칮 2214 | 퍈 2215 | 먁 2216 | 👋 2217 | 캘 2218 | W 2219 | 랳 2220 | 뺙 2221 | 젔 2222 | 냡 2223 | 뭍 2224 | 븜 2225 | 뵌 2226 | 쏼 2227 | 뽝 2228 | 컼 2229 | 껰 2230 | ` 2231 | ㅒ 2232 | 둨 2233 | 쉈 2234 | 🤗 2235 | 콰 2236 | 쨕 2237 | 젴 2238 | 먐 2239 | 읒 2240 | 긌 2241 | 얖 2242 | 쎔 2243 | 헸 2244 | 퐉 2245 | 죰 2246 | 굥 2247 | 곶 2248 | 듷 2249 | 텟 2250 | 쳨 2251 | 땍 2252 | 셬 2253 | 💟 2254 | 햨 2255 | 늕 2256 | 햊 2257 | 🙌 2258 | 앸 2259 | 뜀 2260 | 낍 2261 | 촐 2262 | 뱡 2263 | 챴 2264 | 빋 2265 | 껜 2266 | 잗 2267 | 😻 2268 | 😒 2269 | 😑 2270 | 뵜 2271 | 낞 2272 | 뫃 2273 | ¡ 2274 | 큨 2275 | 눚 2276 | 쐐 2277 | 쯀 2278 | 졓 2279 | 듄 2280 | 쑴 2281 | 샾 2282 | 죙 2283 | 곗 2284 | 뒁 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3617 | 렏 3618 | 켖 3619 | 🌊 3620 | 🏽 3621 | 🥂 3622 | 얟 3623 | 눦 3624 | 첰 3625 | 놁 3626 | 줖 3627 | 빆 3628 | ೭ 3629 | ੧ 3630 | ੭ 3631 | ೨ 3632 | 곜 3633 | 꿜 3634 | 뤔 3635 | 뜡 3636 | 숤 3637 | 뀹 3638 | 즂 3639 | 쎂 3640 | 덛 3641 | 잛 3642 | 💫 3643 | ⛄ 3644 | 춯 3645 | 횜 3646 | 벱 3647 | 솚 3648 | 퐅 3649 | 즵 3650 | 즴 3651 | 꼠 3652 | 뎤 3653 | 쟡 3654 | 놭 3655 | 쟷 3656 | 죈 3657 | ※ 3658 | 쎁 3659 | 뚧 3660 | 💤 3661 | 쯪 3662 | 믁 3663 | 륍 3664 | 풸 3665 | 뷕 3666 | 얅 3667 | 훪 3668 | 돳 3669 | 듓 3670 | 슠 3671 | 엝 3672 | 셓 3673 | 룀 3674 | 쩓 3675 | 쁏 3676 | 둄 3677 | 슫 3678 | 컿 3679 | 쏫 3680 | 럑 3681 | 냊 3682 | 뵫 3683 | 햰 3684 | 댹 3685 | 챼 3686 | 맊 3687 | 띳 3688 | 뗬 3689 | 풩 3690 | 꽙 3691 | 홇 3692 | 헭 3693 | 홃 3694 | 겦 3695 | 괃 3696 | 붔 3697 | 븠 3698 | 끞 3699 | 껓 3700 | 왹 3701 | → 3702 | 好 3703 | 休 3704 | 息 3705 | 吧 3706 | 做 3707 | 菜 3708 | 图 3709 | 书 3710 | 馆 3711 | 쎴 3712 | 쪝 3713 | 봽 3714 | 폅 3715 | 몲 3716 | 텸 3717 | 욛 3718 | 랆 3719 | 룪 3720 | 뚂 3721 | 줳 3722 | 틐 3723 | 쓐 3724 | 💔 3725 | 븱 3726 | 샃 3727 | 덞 3728 | 똻 3729 | 몪 3730 | 🐓 3731 | 툊 3732 | 냦 3733 | 윛 3734 | 햋 3735 | 낿 3736 | 뿃 3737 | 뿁 3738 | 찠 3739 | 욈 3740 | 쬿 3741 | 쓕 3742 | 퉛 3743 | 둩 3744 | 듹 3745 | 랕 3746 | 찓 3747 | 슿 3748 | 힢 3749 | 먇 3750 | 갲 3751 | 셸 3752 | 돀 3753 | 늩 3754 | 홡 3755 | 홛 3756 | 겊 3757 | 돇 3758 | 놎 3759 | 숄 3760 | 휗 3761 | 훱 3762 | 붭 3763 | 욶 3764 | 뜅 3765 | 섦 3766 | 븋 3767 | 똡 3768 | 뮙 3769 | 궘 3770 | 엲 3771 | 쩖 3772 | 꾴 3773 | 씆 3774 | 꾯 3775 | 됗 3776 | 뒵 3777 | 듫 3778 | 켛 3779 | 깢 3780 | 퓐 3781 | 🐫 3782 | 넾 3783 | 엸 3784 | ✍ 3785 | 씈 3786 | 욾 3787 | 쟼 3788 | 쩤 3789 | ̀ 3790 | 솦 3791 | 젰 3792 | 쯕 3793 | 🌼 3794 | 👱 3795 | 쳘 3796 | 웉 3797 | 괁 3798 | 🍼 3799 | 뚸 3800 | 슻 3801 | 🍩 3802 | 슲 3803 | 꽠 3804 | 꽦 3805 | 꿿 3806 | 꽔 3807 | 꽧 3808 | 꽊 3809 | 챻 3810 | 깧 3811 | 욚 3812 | 켸 3813 | 섡 3814 | 🐼 3815 | 띧 3816 | 🎈 3817 | 잂 3818 | 🖐 3819 | 긧 3820 | 🍙 3821 | 횠 3822 | 띸 3823 | 쇾 3824 | 윕 3825 | 쿘 3826 | 뷩 3827 | 틩 3828 | 찲 3829 | 욫 3830 | 륌 3831 | 컺 3832 | 얓 3833 | 뤕 3834 | 봜 3835 | 욵 3836 | 🐩 3837 | 곥 3838 | 쩄 3839 | ー 3840 | 땼 3841 | 힇 3842 | 뮾 3843 | 홰 3844 | 왶 3845 | 밫 3846 | 죶 3847 | 쫔 3848 | 퐙 3849 | 냍 3850 | 쳒 3851 | 🗿 3852 | 🎥 3853 | 컅 3854 | 컃 3855 | 춸 3856 | 툲 3857 | 짒 3858 | 즎 3859 | 줓 3860 | 빷 3861 | 셇 3862 | 웇 3863 | 텂 3864 | 갳 3865 | 겓 3866 | 不 3867 | 是 3868 | 🎮 3869 | 싦 3870 | 먠 3871 | 뻇 3872 | 큱 3873 | 헜 3874 | 놥 3875 | 줚 3876 | 먛 3877 | 音 3878 | 횃 3879 | 🐤 3880 |  3881 | 쩠 3882 | 쐰 3883 | 됳 3884 | 캍 3885 | 걵 3886 | 냃 3887 | 챝 3888 | 헽 3889 | 뉩 3890 | 쀔 3891 | 떃 3892 | 》 3893 | 《 3894 | 뢯 3895 | 볩 3896 | 쾡 3897 | 굗 3898 | 벴 3899 | 걿 3900 | 닟 3901 | 웥 3902 | 쀕 3903 | 젙 3904 | ‼ 3905 | 츃 3906 | 릵 3907 | 맖 3908 | 롔 3909 | 썯 3910 | 갏 3911 | 퓩 3912 | 켭 3913 | 긹 3914 | 윜 3915 | 씋 3916 | 🔒 3917 | ₩ 3918 | 뗑 3919 | 굣 3920 | 👌 3921 | 뿢 3922 | 븃 3923 | 쥨 3924 | 앾 3925 | ♧ 3926 | 🤐 3927 | 쌬 3928 | 😓 3929 | 룤 3930 | 🙅 3931 | ♂ 3932 | 븙 3933 | 졑 3934 | 돍 3935 | 녾 3936 | 짵 3937 | 쒭 3938 | 쏩 3939 | 👻 3940 | 칿 3941 | 뛓 3942 | 떘 3943 | 츌 3944 | 볽 3945 | 삡 3946 | 🌝 3947 | 얆 3948 | 읷 3949 | 릍 3950 | 틹 3951 | 띙 3952 | 럸 3953 | 좈 3954 | 홤 3955 | 븢 3956 | 몿 3957 | 뱜 3958 | 뮁 3959 | 덖 3960 | 턉 3961 | 첧 3962 | 껼 3963 | 쿧 3964 |   3965 | 🍑 3966 | 셪 3967 | 벊 3968 | 쨧 3969 | 댶 3970 | 왙 3971 | 헾 3972 | 뮜 3973 | 뮵 3974 | 귴 3975 | 멯 3976 | 읳 3977 | 쎃 3978 | 흒 3979 | 랲 3980 | 뷍 3981 | 챟 3982 | 딯 3983 | 꺔 3984 | 뽏 3985 | 랋 3986 | 짺 3987 | 솹 3988 | 묙 3989 | 냒 3990 | 쁙 3991 | ！ 3992 | 괒 3993 | 솼 3994 | 듡 3995 | 쨓 3996 | 슏 3997 | 쩰 3998 | 붚 3999 | 럊 4000 | 됝 4001 | 🐴 4002 | 뵏 4003 | 횔 4004 | 숵 4005 | 뭗 4006 | 쫴 4007 | 샪 4008 | 섮 4009 | 💌 4010 | 놏 4011 | 넏 4012 | 둦 4013 | 팹 4014 | 쮄 4015 | 툌 4016 | 뉠 4017 | 촨 4018 | 쐄 4019 | 쐥 4020 | 쫜 4021 | 뱽 4022 | 뎹 4023 | 뢒 4024 | 뫚 4025 | 뽸 4026 | 쩑 4027 | 귑 4028 | 혴 4029 | 읫 4030 | 픟 4031 | 핞 4032 | 앺 4033 | 첳 4034 | 띪 4035 | 텩 4036 | 릻 4037 | 닾 4038 | 콺 4039 | 먖 4040 | 탙 4041 | 놌 4042 | 촞 4043 | 밚 4044 | 맿 4045 | 덨 4046 | △ 4047 | 즑 4048 | 밣 4049 | 챶 4050 | 삮 4051 | 챊 4052 | 킂 4053 | 믖 4054 | 땎 4055 | 먽 4056 | 꽄 4057 | 껟 4058 | 잜 4059 | 곴 4060 | 웄 4061 | 뒺 4062 | 듵 4063 | ε 4064 | 얚 4065 | 솯 4066 | 붊 4067 | 즣 4068 | 벰 4069 | 꾄 4070 | 댏 4071 | 챋 4072 | 햫 4073 | 솏 4074 | 슂 4075 | 쩢 4076 | 껂 4077 | 걑 4078 | 눋 4079 | 퓡 4080 | へ 4081 | 쯐 4082 | 섩 4083 | 욎 4084 | 췋 4085 | 첱 4086 | 턲 4087 | 뷘 4088 | 딶 4089 | 댛 4090 | 빘 4091 | 쩧 4092 | 륰 4093 | 뀄 4094 | 핯 4095 | 촼 4096 | ㄶ 4097 | 둤 4098 | 믘 4099 | 낃 4100 | 냎 4101 | 벶 4102 | 볔 4103 | 텬 4104 | 핷 4105 | 쉏 4106 | 🎄 4107 | 븥 4108 | 뻈 4109 | 낛 4110 | 🌛 4111 | 🌄 4112 | 🐛 4113 | 텽 4114 | 젶 4115 | 턌 4116 | 벷 4117 | 😖 4118 | 봘 4119 | 🙉 4120 | 갵 4121 | 🌻 4122 | 꿏 4123 | 욲 4124 | ☁ 4125 | 셑 4126 | 🏥 4127 | 🍔 4128 | ◎ 4129 | 덲 4130 | 😧 4131 | 밲 4132 | 셖 4133 | ☀ 4134 | 됍 4135 | 쓉 4136 | 딖 4137 | 댚 4138 | 럮 4139 | 쩛 4140 | 엃 4141 | 췩 4142 | 랅 4143 | 밮 4144 | 펃 4145 | 앿 4146 | 퀜 4147 | 뙷 4148 | 푤 4149 | 옧 4150 | 팦 4151 | 킫 4152 | 늰 4153 | 퓍 4154 | 🍯 4155 | 븀 4156 | 엜 4157 | 턍 4158 | 쏮 4159 | 죚 4160 | 뒇 4161 | 뺫 4162 | 쿳 4163 | 뻫 4164 | 튽 4165 | 흓 4166 | 홎 4167 | 촏 4168 | 굻 4169 | 쭏 4170 | 곷 4171 | 덍 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-------------------------------------------------------------------------------- /quickspacer/resources/vocab.txt: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ㅋ 5 | 이 6 | 아 7 | 어 8 | 고 9 | 가 10 | 그 11 | 다 12 | 지 13 | 나 14 | 는 15 | 하 16 | ? 17 | 도 18 | 서 19 | . 20 | 요 21 | 기 22 | 야 23 | 해 24 | 자 25 | 에 26 | 니 27 | 데 28 | 게 29 | 안 30 | 거 31 | 오 32 | 면 33 | 리 34 | 은 35 | 내 36 | 라 37 | 있 38 | 사 39 | 시 40 | 래 41 | 보 42 | 일 43 | 만 44 | 까 45 | 네 46 | 한 47 | 으 48 | 구 49 | 로 50 | 을 51 | 좋 52 | 너 53 | 먹 54 | 제 55 | 마 56 | 랑 57 | 겠 58 | 들 59 | 무 60 | 여 61 | 말 62 | 응 63 | 진 64 | 었 65 | 주 66 | 했 67 | 우 68 | ㅎ 69 | 러 70 | ! 71 | 려 72 | 수 73 | 뭐 74 | 잘 75 | 대 76 | 인 77 | 금 78 | 정 79 | 같 80 | ㅠ 81 | 부 82 | 없 83 | 음 84 | 왜 85 | 더 86 | 알 87 | 저 88 | 생 89 | 근 90 | 난 91 | 짜 92 | 할 93 | 미 94 | 워 95 | 늘 96 | 전 97 | 히 98 | 모 99 | 바 100 | 럼 101 | 것 102 | 스 103 | 많 104 | 간 105 | 건 106 | 애 107 | 상 108 | 싶 109 | 웅 110 | 와 111 | 렇 112 | 빠 113 | 좀 114 | 귀 115 | 되 116 | 예 117 | 집 118 | 심 119 | 냥 120 | 냐 121 | 못 122 | ~ 123 | 때 124 | 았 125 | 화 126 | 치 127 | 를 128 | 쪽 129 | , 130 | 런 131 | 장 132 | 르 133 | 중 134 | 연 135 | 비 136 | 의 137 | 봐 138 | 맞 139 | 각 140 | 신 141 | 돼 142 | 당 143 | 조 144 | 소 145 | 분 146 | 원 147 | ㅜ 148 | 찮 149 | 디 150 | 공 151 | 람 152 | 피 153 | 잖 154 | 두 155 | 괜 156 | 언 157 | 날 158 | 실 159 | 테 160 | 뽀 161 | 세 162 | 배 163 | 동 164 | 직 165 | 않 166 | 걸 167 | 1 168 | 친 169 | 운 170 | 누 171 | 방 172 | 왔 173 | 갈 174 | 얼 175 | 번 176 | 남 177 | 엄 178 | 님 179 | 줄 180 | 단 181 | 학 182 | 잠 183 | 드 184 | 밥 185 | 행 186 | 물 187 | 형 188 | 끝 189 | 든 190 | 울 191 | 른 192 | 터 193 | 속 194 | 재 195 | 머 196 | 용 197 | 입 198 | 줘 199 | 헤 200 | 문 201 | 트 202 | 약 203 | 영 204 | 져 205 | 청 206 | 발 207 | 교 208 | 버 209 | 살 210 | 개 211 | 경 212 | 힘 213 | 올 214 | 민 215 | 계 216 | 노 217 | 싫 218 | 쁘 219 | 또 220 | 몰 221 | 따 222 | 맛 223 | 긴 224 | 적 225 | 차 226 | 유 227 | 파 228 | 군 229 | 졸 230 | 감 231 | 엉 232 | 프 233 | 선 234 | 불 235 | 놀 236 | 찍 237 | 2 238 | 준 239 | 빨 240 | 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결 366 | 먼 367 | 넌 368 | 걱 369 | 앞 370 | 싸 371 | 뭘 372 | 였 373 | 께 374 | 온 375 | 케 376 | 존 377 | 티 378 | 커 379 | 돈 380 | 체 381 | 럽 382 | 초 383 | ^ 384 | 엔 385 | 듯 386 | 징 387 | 병 388 | 벌 389 | 찾 390 | 능 391 | 증 392 | 맨 393 | 앗 394 | 억 395 | 릴 396 | 된 397 | 절 398 | 돌 399 | 년 400 | 쳐 401 | 밌 402 | 듣 403 | 깨 404 | 외 405 | 잇 406 | 력 407 | 궁 408 | 떡 409 | 슈 410 | 굴 411 | 몸 412 | 튼 413 | ♡ 414 | 꼭 415 | 딱 416 | 충 417 | 웠 418 | 밖 419 | 메 420 | 꿈 421 | 퇴 422 | 4 423 | 항 424 | 평 425 | 색 426 | 백 427 | 셨 428 | 순 429 | 취 430 | 꺼 431 | 챙 432 | 험 433 | 찌 434 | ㅅ 435 | 폰 436 | 옷 437 | 끄 438 | 며 439 | 표 440 | 낼 441 | 멋 442 | 잔 443 | 짱 444 | 최 445 | 흠 446 | 필 447 | 확 448 | 겟 449 | 될 450 | 텐 451 | 월 452 | 밤 453 | 변 454 | 둘 455 | 란 456 | 놓 457 | 꼬 458 | 즘 459 | 혜 460 | 흐 461 | 몇 462 | 합 463 | 종 464 | 똑 465 | 옆 466 | 플 467 | 엇 468 | 등 469 | 움 470 | 채 471 | 책 472 | 욕 473 | 뎅 474 | 풀 475 | 줬 476 | 페 477 | 겼 478 | 투 479 | 곧 480 | 6 481 | 랐 482 | 격 483 | 쳤 484 | 얌 485 | 멀 486 | 혀 487 | 닝 488 | 윤 489 | 뒤 490 | 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1436 | 큭 1437 | C 1438 | 슥 1439 | 풉 1440 | 쭝 1441 | 섰 1442 | 봇 1443 | 깤 1444 | 쬐 1445 | 폈 1446 | 툭 1447 | 꿇 1448 | 몹 1449 | 돕 1450 | 팽 1451 | 풋 1452 | 엮 1453 | 넥 1454 | 빚 1455 | 뎁 1456 | 쿡 1457 | 썻 1458 | 쎠 1459 | M 1460 | 팥 1461 | 쎈 1462 | 왈 1463 | D 1464 | ㆍ 1465 | n 1466 | 젛 1467 | 떄 1468 | 쏭 1469 | 룹 1470 | 읏 1471 | 읎 1472 | 둡 1473 | 샹 1474 | 쏠 1475 | 덧 1476 | 둠 1477 | 엊 1478 | 댘 1479 | 엨 1480 | 냔 1481 | 콱 1482 | 닛 1483 | 렙 1484 | 갯 1485 | 쐬 1486 | 츤 1487 | 홓 1488 | 얹 1489 | 숴 1490 | 😙 1491 | 롸 1492 | 옄 1493 | 뀜 1494 | 걀 1495 | 짴 1496 | 햤 1497 | 렜 1498 | 떳 1499 | 굵 1500 | 엏 1501 | 퐁 1502 | 췌 1503 | 뗘 1504 | 뺀 1505 | 솥 1506 | 셜 1507 | 쉅 1508 | 젝 1509 | 퍄 1510 | 횟 1511 | 쌔 1512 | 헴 1513 | 💋 1514 | 맣 1515 | 돗 1516 | 흙 1517 | 핡 1518 | 팁 1519 | 쭤 1520 | 😁 1521 | 깄 1522 | 숍 1523 | 얜 1524 | 뀔 1525 | 짭 1526 | G 1527 | 믹 1528 | 뭥 1529 | 놧 1530 | ☆ 1531 | 앟 1532 | 횡 1533 | l 1534 | 츈 1535 | 😗 1536 | 텼 1537 | 갇 1538 | 꺾 1539 | 읔 1540 | 젓 1541 | 뎀 1542 | 젼 1543 | 됭 1544 | 퀘 1545 | 헙 1546 | 웩 1547 | f 1548 | P 1549 | 홋 1550 | 떽 1551 | 챱 1552 | r 1553 | 렘 1554 | 옶 1555 | 잨 1556 | 챠 1557 | 쟨 1558 | X 1559 | 밭 1560 | O 1561 | 슐 1562 | 앖 1563 | 💜 1564 | 뱀 1565 | 삘 1566 | 탠 1567 | 얗 1568 | 🏻 1569 | 짚 1570 | 왛 1571 | 폼 1572 | 힉 1573 | 몀 1574 | 펌 1575 | 숏 1576 | 킼 1577 | 떰 1578 | 딥 1579 | 깠 1580 | 펙 1581 | 뽁 1582 | 왁 1583 | 켯 1584 | ㄳ 1585 | 괄 1586 | 뵤 1587 | 쭁 1588 | 훠 1589 | 텝 1590 | 귓 1591 | 킵 1592 | 맠 1593 | ] 1594 | 눼 1595 | 😭 1596 | 텅 1597 | 눅 1598 | 맺 1599 | 펄 1600 | 멎 1601 | u 1602 | 햏 1603 | 깈 1604 | 왘 1605 | 갛 1606 | 쓱 1607 | ☺ 1608 | 솟 1609 | 혓 1610 | 칵 1611 | 빕 1612 | 슙 1613 | h 1614 | 샥 1615 | 헥 1616 | w 1617 | 쒸 1618 | 뚠 1619 | 쨘 1620 | 녱 1621 | 깁 1622 | 츕 1623 | 젯 1624 | 듭 1625 | 뤘 1626 | 싳 1627 | [ 1628 | 훼 1629 | 뻣 1630 | 훟 1631 | 짰 1632 | 갬 1633 | 씸 1634 | 앱 1635 | 븅 1636 | ᆞ 1637 | 퀄 1638 | 웰 1639 | 칰 1640 | 먕 1641 | 셍 1642 | 😚 1643 | 맏 1644 | 넛 1645 | 꾜 1646 | 쫙 1647 | L 1648 | K 1649 | 쏴 1650 | 힙 1651 | 꿧 1652 | 킥 1653 | 뀽 1654 | V 1655 | 늉 1656 | 햅 1657 | 흣 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1769 | 휠 1770 | 륜 1771 | 듈 1772 | 핼 1773 | 췟 1774 | 짆 1775 | 뭡 1776 | Z 1777 | 읊 1778 | 쭘 1779 | 펫 1780 | 얶 1781 | 눴 1782 | 겝 1783 | 켁 1784 | 렄 1785 | 힠 1786 | I 1787 | 캉 1788 | 쟐 1789 | 묭 1790 | 꼐 1791 | 핮 1792 | 쩨 1793 | 뛴 1794 | 첸 1795 | • 1796 | 쌰 1797 | 😄 1798 | 팰 1799 | 🙈 1800 | 겧 1801 | 쥼 1802 | 😢 1803 | 😅 1804 | 쭐 1805 | ★ 1806 | 챔 1807 | 뛸 1808 | 멩 1809 | 퀸 1810 | 캭 1811 | 쓔 1812 | 핟 1813 | 롴 1814 | 깸 1815 | 셰 1816 | 얒 1817 | 뷴 1818 | 😳 1819 | 쯰 1820 | 겆 1821 | 룜 1822 | 놬 1823 | 쌉 1824 | 춍 1825 | 슌 1826 | 헨 1827 | 꿰 1828 | 끠 1829 | H 1830 | 웈 1831 | 맷 1832 | 쟉 1833 | 춝 1834 | 쨜 1835 | 톰 1836 | 잰 1837 | 넬 1838 | 엡 1839 | 킷 1840 | 앀 1841 | 퀵 1842 | 😏 1843 | 꿋 1844 | 댑 1845 | 큣 1846 | 똔 1847 | 됌 1848 | 큔 1849 | 💛 1850 | 씼 1851 | 🙏 1852 | 샛 1853 | 겎 1854 | 햣 1855 | 땟 1856 | 픕 1857 | 넜 1858 | 뭄 1859 | ㅝ 1860 | 💙 1861 | 찻 1862 | 켬 1863 | 앍 1864 | 젭 1865 | 춉 1866 | 즛 1867 | 틋 1868 | F 1869 | 앎 1870 | 쥥 1871 | 랖 1872 | 붐 1873 | 얐 1874 | 씐 1875 | j 1876 | U 1877 | 탆 1878 | 켈 1879 | 촵 1880 | 큽 1881 | Q 1882 | 촛 1883 | 냌 1884 | 땽 1885 | 짇 1886 | 밨 1887 | 앴 1888 | 궜 1889 | 궂 1890 | 싴 1891 | 뎄 1892 | ’ 1893 | 뭨 1894 | 껨 1895 | 읕 1896 | 읺 1897 | 빱 1898 | 쬽 1899 | 뮹 1900 | 훑 1901 | 줏 1902 | 뗄 1903 | ㅢ 1904 | 욌 1905 | E 1906 | 깟 1907 | 옇 1908 | 믕 1909 | 앆 1910 | 쁑 1911 | 꿑 1912 | 끅 1913 | 튈 1914 | 쵝 1915 | 옸 1916 | 젬 1917 | 짙 1918 | 굔 1919 | 틍 1920 | 땋 1921 | 쭙 1922 | 듸 1923 | 휙 1924 | J 1925 | 뽜 1926 | 왬 1927 | 횽 1928 | 읗 1929 | 늪 1930 | 웜 1931 | N 1932 | 옿 1933 | 냘 1934 | 룻 1935 | 뮨 1936 | 밎 1937 | 곘 1938 | 튄 1939 | 퇵 1940 | 곽 1941 | 릐 1942 | 맬 1943 | 쎙 1944 | 랃 1945 | Y 1946 | 낱 1947 | 탸 1948 | 씅 1949 | 싣 1950 | 끜 1951 | 렝 1952 | 챘 1953 | 맟 1954 | 켔 1955 | 훤 1956 | 켄 1957 | 뉭 1958 | 흨 1959 | 덯 1960 | 켤 1961 | 뷸 1962 | 쉘 1963 | 녘 1964 | 핌 1965 | 숯 1966 | 😣 1967 | 👏 1968 | 즤 1969 | 쳥 1970 | 츙 1971 | 핬 1972 | 킈 1973 | 벘 1974 | 쎵 1975 | 싷 1976 | 롷 1977 | 숱 1978 | 😐 1979 | 넋 1980 | 맸 1981 | 죤 1982 | 텄 1983 | 됔 1984 | 씰 1985 | 띰 1986 | ㅖ 1987 | 퍙 1988 | 냬 1989 | 😬 1990 | 💖 1991 | 쫒 1992 | 뺴 1993 | 궤 1994 | 켘 1995 | 툽 1996 | 뵐 1997 | 탬 1998 | 봌 1999 | 쯉 2000 | 잴 2001 | 쑈 2002 | 뭬 2003 | 캌 2004 | 슼 2005 | 믈 2006 | 둉 2007 | 컄 2008 | 쑝 2009 | 냑 2010 | 쿱 2011 | 뵹 2012 | 겋 2013 | 띤 2014 | 흩 2015 | 뉑 2016 | 됀 2017 | 😯 2018 | 휩 2019 | 꺽 2020 | 맀 2021 | 샸 2022 | 쪗 2023 | 👀 2024 | 쉑 2025 | 쌋 2026 | 죻 2027 | 눔 2028 | 칞 2029 | 팤 2030 | 렠 2031 | 붸 2032 | 뷥 2033 | 셌 2034 | 짹 2035 | 샄 2036 | 놰 2037 | 🐕 2038 | ☞ 2039 | ☜ 2040 | 섣 2041 | & 2042 | 쨀 2043 | 윳 2044 | 돟 2045 | 😀 2046 | 몈 2047 | 줰 2048 | 굠 2049 | 켠 2050 | 늗 2051 | 첵 2052 | 😥 2053 | 뮈 2054 | 똬 2055 | 쪈 2056 | 촥 2057 | 걌 2058 | 앚 2059 | 쎅 2060 | 펩 2061 | 뗭 2062 | 쌕 2063 | 뱔 2064 | 혬 2065 | 촙 2066 | 쥔 2067 | 툼 2068 | 뵨 2069 | 앳 2070 | 볐 2071 | 먿 2072 | 먾 2073 | 뚬 2074 | 셥 2075 | 봬 2076 | 싀 2077 | 켕 2078 | 쉣 2079 | 컁 2080 | 짛 2081 | 낵 2082 | 닜 2083 | 삑 2084 | 뗐 2085 | 봊 2086 | 잫 2087 | 뚀 2088 | 🤔 2089 | 곺 2090 | 챌 2091 | 뺌 2092 | 쟜 2093 | 믐 2094 | 쥭 2095 | 싰 2096 | 꺙 2097 | 닪 2098 | 뽄 2099 | 😞 2100 | 꿩 2101 | 왐 2102 | 큘 2103 | 갹 2104 | 펏 2105 | 쨰 2106 | 뼛 2107 | 잽 2108 | 휑 2109 | 욀 2110 | 쒀 2111 | 걐 2112 | 😛 2113 | 갼 2114 | 챕 2115 | 솨 2116 | 댠 2117 | 덱 2118 | 핍 2119 | 뉨 2120 | 뀰 2121 | ㅘ 2122 | 읅 2123 | 럄 2124 | 삔 2125 | 쎀 2126 | 캇 2127 | 둿 2128 | 펨 2129 | 퐝 2130 | 닊 2131 | 쿜 2132 | 뉵 2133 | 뭠 2134 | 읻 2135 | 🙆 2136 | 왭 2137 | 윅 2138 | 꼿 2139 | ㅙ 2140 | 멓 2141 | 🐶 2142 | 졈 2143 | 쟛 2144 | 탤 2145 | 톧 2146 | 옜 2147 | 믾 2148 | 휭 2149 | 갴 2150 | 쬠 2151 | 싄 2152 | 갭 2153 | 뾱 2154 | 옝 2155 | 쟘 2156 | 춌 2157 | 떫 2158 | 앝 2159 | 쁩 2160 | 찯 2161 | 찧 2162 | 옘 2163 | 맢 2164 | 쥽 2165 | 휜 2166 | 컾 2167 | 퓌 2168 | 헵 2169 | 긑 2170 | 챈 2171 | 냣 2172 | 왑 2173 | 섀 2174 | 폿 2175 | 콸 2176 | 귐 2177 | 릏 2178 | 푠 2179 | 끛 2180 | ‍ 2181 | 놩 2182 | 쿸 2183 | 쿙 2184 | 믓 2185 | 귯 2186 | 빸 2187 | 쟙 2188 | 횬 2189 | 춧 2190 | 잒 2191 | 힁 2192 | 삣 2193 | 뷱 2194 | 렣 2195 | 닏 2196 | 짯 2197 | 츰 2198 | 깉 2199 | 끵 2200 | 좊 2201 | 빻 2202 | 좃 2203 | 녓 2204 | 쐈 2205 | 햬 2206 | 젆 2207 | 귿 2208 | 캎 2209 | 😔 2210 | 캥 2211 | 똫 2212 | 텁 2213 | 칮 2214 | 퍈 2215 | 먁 2216 | 👋 2217 | 캘 2218 | W 2219 | 랳 2220 | 뺙 2221 | 젔 2222 | 냡 2223 | 뭍 2224 | 븜 2225 | 뵌 2226 | 쏼 2227 | 뽝 2228 | 컼 2229 | 껰 2230 | ` 2231 | ㅒ 2232 | 둨 2233 | 쉈 2234 | 🤗 2235 | 콰 2236 | 쨕 2237 | 젴 2238 | 먐 2239 | 읒 2240 | 긌 2241 | 얖 2242 | 쎔 2243 | 헸 2244 | 퐉 2245 | 죰 2246 | 굥 2247 | 곶 2248 | 듷 2249 | 텟 2250 | 쳨 2251 | 땍 2252 | 셬 2253 | 💟 2254 | 햨 2255 | 늕 2256 | 햊 2257 | 🙌 2258 | 앸 2259 | 뜀 2260 | 낍 2261 | 촐 2262 | 뱡 2263 | 챴 2264 | 빋 2265 | 껜 2266 | 잗 2267 | 😻 2268 | 😒 2269 | 😑 2270 | 뵜 2271 | 낞 2272 | 뫃 2273 | ¡ 2274 | 큨 2275 | 눚 2276 | 쐐 2277 | 쯀 2278 | 졓 2279 | 듄 2280 | 쑴 2281 | 샾 2282 | 죙 2283 | 곗 2284 | 뒁 2285 | 핱 2286 | 뒙 2287 | 밯 2288 | 띁 2289 | 츼 2290 | q 2291 | 캴 2292 | 뽈 2293 | 봑 2294 | 웋 2295 | 몃 2296 | | 2297 | 핶 2298 | 칡 2299 | 츨 2300 | 폄 2301 | 밪 2302 | 럐 2303 | 👶 2304 | 🌙 2305 | 쥑 2306 | 껒 2307 | 죳 2308 | 짲 2309 | 쪜 2310 | 뉍 2311 | 쮹 2312 | 줒 2313 | 듐 2314 | 쳉 2315 | 넿 2316 | 퉈 2317 | 졀 2318 | 럌 2319 | 쓩 2320 | 얃 2321 | 궨 2322 | 뎠 2323 | 횐 2324 | 낡 2325 | 츅 2326 | 탇 2327 | 괭 2328 | ㅟ 2329 | 숖 2330 | 짼 2331 | 듁 2332 | 🎶 2333 | 랒 2334 | 샆 2335 | 죸 2336 | 밐 2337 | 녠 2338 | 꿱 2339 | 쮜 2340 | 쿗 2341 | 늫 2342 | 볻 2343 | 뱍 2344 | 웍 2345 | 햘 2346 | 줴 2347 | 몄 2348 | 툐 2349 | “ 2350 | ” 2351 | 옾 2352 | 캅 2353 | 탯 2354 | 슛 2355 | 헑 2356 | 첼 2357 | ‘ 2358 | 쯍 2359 | 묽 2360 | 뙇 2361 | 싐 2362 | 엳 2363 | 헀 2364 | 늄 2365 | 좨 2366 | 😠 2367 | 뮴 2368 | 럇 2369 | 뇬 2370 | 섷 2371 | 땈 2372 | 뤈 2373 | 췃 2374 | 꽇 2375 | 흌 2376 | 🌸 2377 | 좡 2378 | 🔥 2379 | } 2380 | 쉭 2381 | 😡 2382 | 줅 2383 | 춪 2384 | 먗 2385 | 닁 2386 | 뉜 2387 | 😪 2388 | 🐥 2389 | 🙄 2390 | 랸 2391 | 슺 2392 | 샨 2393 | × 2394 | 왂 2395 | 햿 2396 | 샅 2397 | 곻 2398 | 퓽 2399 | 햠 2400 | 쒯 2401 | 녬 2402 | 텡 2403 | 웂 2404 | 떢 2405 | 숰 2406 | 뷁 2407 | 렂 2408 | 쎼 2409 | 뺭 2410 | 뚕 2411 | 댬 2412 | 짔 2413 | 챵 2414 | 끕 2415 | 꽌 2416 | 괵 2417 | 귕 2418 | 푱 2419 | 껠 2420 | 졷 2421 | 폇 2422 | 굡 2423 | 팼 2424 | 딛 2425 | 늨 2426 | 딬 2427 | 얺 2428 | 찟 2429 | 👉 2430 | 👈 2431 | ⭐ 2432 | 갉 2433 | 쎘 2434 | 몯 2435 | 곯 2436 | 읐 2437 | 퇘 2438 | 팻 2439 | 붘 2440 | 뎊 2441 | 💘 2442 | ㅄ 2443 | 놑 2444 | 좔 2445 | 👄 2446 | 💩 2447 | 랊 2448 | 뜍 2449 | ¿ 2450 | 쏨 2451 | 샇 2452 | 덬 2453 | 옂 2454 | 앛 2455 | 왯 2456 | · 2457 | 휸 2458 | 쑨 2459 | 햔 2460 | 뱐 2461 | 럾 2462 | 묯 2463 | 윷 2464 | 궐 2465 | 💝 2466 | 깇 2467 | 띾 2468 | 춋 2469 | 첮 2470 | 췻 2471 | 귝 2472 | 꺠 2473 | 돤 2474 | 썪 2475 | 쯜 2476 | 넒 2477 | 똭 2478 | 녑 2479 | 챃 2480 | 깦 2481 | 쳔 2482 | 굑 2483 | 왝 2484 | 😤 2485 | 흗 2486 | 뇹 2487 | 뛔 2488 | 쨥 2489 | 쵹 2490 | 훕 2491 | 뤃 2492 | 퉜 2493 | 챀 2494 | 눤 2495 | 죡 2496 | 샬 2497 | 잍 2498 | 벵 2499 | ㄻ 2500 | 흿 2501 | 묠 2502 | 싁 2503 | 챳 2504 | 릌 2505 | 뢩 2506 | 콥 2507 | 봔 2508 | 촘 2509 | 텻 2510 | 젹 2511 | 놯 2512 | 캤 2513 | 퀭 2514 | 죗 2515 | 쯩 2516 | 즇 2517 | 둏 2518 | 뻡 2519 | 똨 2520 | 텃 2521 | 닽 2522 | 멐 2523 | 퀏 2524 | 귾 2525 | 놐 2526 | 꽹 2527 | 튬 2528 | ´ 2529 | 닞 2530 | 깋 2531 | 픙 2532 | 솓 2533 | 섁 2534 | 걜 2535 | 맂 2536 | 눀 2537 | 쨤 2538 | 쁍 2539 | 렿 2540 | 톼 2541 | 렺 2542 | 쁭 2543 | 쟠 2544 | 뚁 2545 | 짞 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3989 | 냒 3990 | 쁙 3991 | ！ 3992 | 괒 3993 | 솼 3994 | 듡 3995 | 쨓 3996 | 슏 3997 | 쩰 3998 | 붚 3999 | 럊 4000 | 됝 4001 | 🐴 4002 | 뵏 4003 | 횔 4004 | 숵 4005 | 뭗 4006 | 쫴 4007 | 샪 4008 | 섮 4009 | 💌 4010 | 놏 4011 | 넏 4012 | 둦 4013 | 팹 4014 | 쮄 4015 | 툌 4016 | 뉠 4017 | 촨 4018 | 쐄 4019 | 쐥 4020 | 쫜 4021 | 뱽 4022 | 뎹 4023 | 뢒 4024 | 뫚 4025 | 뽸 4026 | 쩑 4027 | 귑 4028 | 혴 4029 | 읫 4030 | 픟 4031 | 핞 4032 | 앺 4033 | 첳 4034 | 띪 4035 | 텩 4036 | 릻 4037 | 닾 4038 | 콺 4039 | 먖 4040 | 탙 4041 | 놌 4042 | 촞 4043 | 밚 4044 | 맿 4045 | 덨 4046 | △ 4047 | 즑 4048 | 밣 4049 | 챶 4050 | 삮 4051 | 챊 4052 | 킂 4053 | 믖 4054 | 땎 4055 | 먽 4056 | 꽄 4057 | 껟 4058 | 잜 4059 | 곴 4060 | 웄 4061 | 뒺 4062 | 듵 4063 | ε 4064 | 얚 4065 | 솯 4066 | 붊 4067 | 즣 4068 | 벰 4069 | 꾄 4070 | 댏 4071 | 챋 4072 | 햫 4073 | 솏 4074 | 슂 4075 | 쩢 4076 | 껂 4077 | 걑 4078 | 눋 4079 | 퓡 4080 | へ 4081 | 쯐 4082 | 섩 4083 | 욎 4084 | 췋 4085 | 첱 4086 | 턲 4087 | 뷘 4088 | 딶 4089 | 댛 4090 | 빘 4091 | 쩧 4092 | 륰 4093 | 뀄 4094 | 핯 4095 | 촼 4096 | ㄶ 4097 | 둤 4098 | 믘 4099 | 낃 4100 | 냎 4101 | 벶 4102 | 볔 4103 | 텬 4104 | 핷 4105 | 쉏 4106 | 🎄 4107 | 븥 4108 | 뻈 4109 | 낛 4110 | 🌛 4111 | 🌄 4112 | 🐛 4113 | 텽 4114 | 젶 4115 | 턌 4116 | 벷 4117 | 😖 4118 | 봘 4119 | 🙉 4120 | 갵 4121 | 🌻 4122 | 꿏 4123 | 욲 4124 | ☁ 4125 | 셑 4126 | 🏥 4127 | 🍔 4128 | ◎ 4129 | 덲 4130 | 😧 4131 | 밲 4132 | 셖 4133 | ☀ 4134 | 됍 4135 | 쓉 4136 | 딖 4137 | 댚 4138 | 럮 4139 | 쩛 4140 | 엃 4141 | 췩 4142 | 랅 4143 | 밮 4144 | 펃 4145 | 앿 4146 | 퀜 4147 | 뙷 4148 | 푤 4149 | 옧 4150 | 팦 4151 | 킫 4152 | 늰 4153 | 퓍 4154 | 🍯 4155 | 븀 4156 | 엜 4157 | 턍 4158 | 쏮 4159 | 죚 4160 | 뒇 4161 | 뺫 4162 | 쿳 4163 | 뻫 4164 | 튽 4165 | 흓 4166 | 홎 4167 | 촏 4168 | 굻 4169 | 쭏 4170 | 곷 4171 | 덍 4172 | 궆 4173 | 꺋 4174 | □ 4175 | 짃 4176 | 뒫 4177 | 뤗 4178 | 빂 4179 | 츳 4180 | 렅 4181 | 훝 4182 | 젋 4183 | 떅 4184 | 혇 4185 | 탛 4186 | 밳 4187 | 낹 4188 | 붅 4189 | { 4190 | 렢 4191 | 왅 4192 | 뀟 4193 | 짶 4194 | 쓿 4195 | 슧 4196 | 롲 4197 | 쳑 4198 | 곙 4199 | 죴 4200 | 쾩 4201 | 큄 4202 | 꺟 4203 | 🧐 4204 | 켙 4205 | ↑ 4206 | 쁟 4207 | 뭀 4208 | 몝 4209 | 쳬 4210 | 솽 4211 | 쓍 4212 | 덦 4213 | 😦 4214 | 듖 4215 | 꼮 4216 | 맫 4217 | 곃 4218 | 굇 4219 | 얩 4220 | 还 4221 | 没 4222 | 终 4223 | で 4224 | き 4225 | る 4226 | 子 4227 | 多 4228 | 女 4229 | 듑 4230 | 🎎 4231 | 틘 4232 | 폘 4233 | 쟸 4234 | 륃 4235 | 넁 4236 | 펗 4237 | 뱈 4238 | 꽜 4239 | 롘 4240 | 찴 4241 | 솃 4242 | 윧 4243 | 폨 4244 | ● 4245 | ٩ 4246 | ۶ 4247 | 럳 4248 | 곦 4249 | 꾺 4250 | 쿻 4251 | -------------------------------------------------------------------------------- /quickspacer/spacer.py: -------------------------------------------------------------------------------- 1 | import os 2 | from typing import Iterable 3 | 4 | import tensorflow as tf 5 | 6 | from .constant import DEFAULT_MODEL_PATH 7 | 8 | 9 | class Spacer: 10 | def __init__(self, saved_model_dir=None, level=1): 11 | if saved_model_dir is None: 12 | assert level in (1, 2, 3), "level should be one of (1,2,3)!" 13 | saved_model_dir = os.path.join(DEFAULT_MODEL_PATH, str(level)) 14 | 15 | self.model = tf.saved_model.load(saved_model_dir) 16 | 17 | def space(self, sentences: Iterable[str], batch_size=None) -> Iterable[str]: 18 | all_spaced_sentences = [] 19 | 20 | if batch_size is None: 21 | batch_size = len(sentences) 22 | 23 | while sentences: 24 | batch_sentences = tf.constant(sentences[:batch_size]) 25 | spaced_sentences = self.model.signatures["serving_default"](batch_sentences)["spaced_sentences"] 26 | all_spaced_sentences.extend([sentence.numpy().decode("utf-8") for sentence in spaced_sentences]) 27 | sentences = sentences[batch_size:] 28 | return all_spaced_sentences 29 | -------------------------------------------------------------------------------- /quickspacer/utils.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.keras import backend as K 3 | 4 | 5 | def learning_rate_scheduler(num_epochs, max_learning_rate, min_learninga_rate=1e-7): 6 | lr_delta = (max_learning_rate - min_learninga_rate) / num_epochs 7 | 8 | def _scheduler(epoch, lr): 9 | return lr - lr_delta 10 | 11 | return _scheduler 12 | 13 | 14 | def f1_loss(y_true, y_pred): 15 | y_true = tf.cast(y_true, tf.float32) 16 | y_pred = tf.cast(y_pred, tf.float32) 17 | 18 | TP = tf.reduce_sum(y_true * y_pred, axis=0) 19 | FP = tf.reduce_sum((1 - y_true) * y_pred, axis=0) 20 | FN = tf.reduce_sum(y_true * (1 - y_pred), axis=0) 21 | 22 | precision = TP / (TP + FP + K.epsilon()) 23 | recall = TP / (TP + FN + K.epsilon()) 24 | 25 | f1 = 2 * precision * recall / (precision + recall + K.epsilon()) 26 | f1 = tf.where(tf.math.is_nan(f1), tf.zeros_like(f1), f1) 27 | return 1 - tf.reduce_mean(f1) 28 | 29 | 30 | def recall(y_true, y_pred): 31 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 32 | possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) 33 | recall = true_positives / (possible_positives + K.epsilon()) 34 | return recall 35 | 36 | 37 | def precision(y_true, y_pred): 38 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 39 | predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) 40 | precision = true_positives / (predicted_positives + K.epsilon()) 41 | return precision 42 | 43 | 44 | def f1_score(y_true, y_pred): 45 | precision_score = precision(y_true, y_pred) 46 | recall_socre = recall(y_true, y_pred) 47 | return 2 * (precision_score * recall_socre) / (precision_score + recall_socre + K.epsilon()) 48 | -------------------------------------------------------------------------------- /requirements-dev.txt: -------------------------------------------------------------------------------- 1 | black 2 | isort 3 | pytest 4 | pytest-cov 5 | codecov 6 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tensorflow>=2.1 2 | tensorflow-addons 3 | tensorflowjs 4 | tqdm 5 | -------------------------------------------------------------------------------- /scripts/check_time.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from time import time 4 | 5 | import tensorflow as tf 6 | 7 | from quickspacer.constant import DEFAULT_MODEL_PATH 8 | from quickspacer.spacer import Spacer 9 | 10 | if __name__ == "__main__": 11 | parser = argparse.ArgumentParser() 12 | parser.add_argument("--saved-model-dir", type=str, default=os.path.join(DEFAULT_MODEL_PATH, "1")) 13 | parser.add_argument("--input-path", type=str, required=True) 14 | parser.add_argument("--output-path", type=str, default=None) 15 | parser.add_argument("--encoding", type=str, default="utf-8") 16 | args = parser.parse_args() 17 | 18 | spacer = Spacer(saved_model_dir=args.saved_model_dir) 19 | with open(args.input_path, encoding=args.encoding) as f: 20 | sentences = f.readlines() 21 | print(f"[*] Loaded file from {args.input_path}") 22 | 23 | start_time = time() 24 | spaced_sentences = spacer.space(sentences) 25 | elapsed_time = time() - start_time 26 | print("[*] Finished Inferece") 27 | print(f"[*] Elaspsed Time: {elapsed_time:.8f} seconds") 28 | 29 | if args.output_path: 30 | with open(args.output_path, "w") as fout: 31 | fout.writelines(spaced_sentences) 32 | print(f"[*] Saved spaced file to '{args.output_path}'") 33 | -------------------------------------------------------------------------------- /scripts/convert_to_savedmodel.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | from functools import partial 4 | 5 | import tensorflow as tf 6 | 7 | from quickspacer import model 8 | from quickspacer.constant import DEFAULT_OOV_INDEX, DEFAULT_VOCAB_PATH 9 | from quickspacer.data import load_vocab, sentence_to_index 10 | 11 | if __name__ == "__main__": 12 | # fmt: off 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument("--model-name", type=str, default="ConvSpacer1", help="Model class name in quickspacer.model") 15 | parser.add_argument("--model-config-file", type=str, default="configs/conv1-spacer-config.json", help="Config file path for model") 16 | parser.add_argument("--model-weight-path", type=str, required=True, help="Model weight file path saved in training") 17 | parser.add_argument("--vocab-path", type=str, default=DEFAULT_VOCAB_PATH, help="Vocab file path") 18 | parser.add_argument("--oov-index", type=int, default=DEFAULT_OOV_INDEX, help="OOV Index") 19 | parser.add_argument("--output-path", type=str, default="saved_spacer_model/1", help="Savedmodel path") 20 | parser.add_argument("--threshold", type=float, default=0.5, help="Threshold to space") 21 | args = parser.parse_args() 22 | # fmt: on 23 | 24 | with open(args.model_config_file) as f: 25 | model = getattr(model, args.model_name)(**json.load(f)) 26 | model.load_weights(args.model_weight_path) 27 | print("Loaded weights of model") 28 | 29 | vocab = load_vocab(args.vocab_path, args.oov_index) 30 | print("Loaded vocab") 31 | 32 | @tf.function(input_signature=[tf.TensorSpec((None,), tf.string)]) 33 | def space_texts(texts): 34 | # Construct dataset 35 | tokens = tf.map_fn( 36 | partial(sentence_to_index, vocab=vocab), 37 | texts, 38 | fn_output_signature=tf.RaggedTensorSpec([None], tf.int32, ragged_rank=0), 39 | ).to_tensor() 40 | 41 | # Model inference 42 | output = model(tokens) 43 | 44 | # Make spaced sentence 45 | texts = tf.strings.unicode_split(texts, "UTF-8").to_tensor() 46 | spaced_sentences = tf.where(output < args.threshold, texts, texts + " ") 47 | spaced_sentences = tf.strings.strip(tf.strings.reduce_join(spaced_sentences, axis=1)) 48 | 49 | return {"spaced_sentences": spaced_sentences} 50 | 51 | @tf.function(input_signature=[tf.TensorSpec((None, None), tf.int32)]) 52 | def model_inference(tokens): 53 | outputs = model(tokens) 54 | return outputs 55 | 56 | model.vocab = vocab 57 | tf.saved_model.save( 58 | model, 59 | args.output_path, 60 | signatures={"serving_default": space_texts, "model_inference": model_inference}, 61 | ) 62 | -------------------------------------------------------------------------------- /scripts/convert_to_tfjsmodel.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import tensorflowjs 4 | 5 | if __name__ == "__main__": 6 | # fmt: off 7 | parser = argparse.ArgumentParser() 8 | parser.add_argument("--saved-model-path", default="saved_spacer_model/1", help="Savedmodel path generated by convert_to_savedmodel") 9 | parser.add_argument("--output-path", default="tfjs_spacer_model", help="Path to save tfjs model") 10 | parser.add_argument("--control_flow_v2", action="store_false") 11 | parser.add_argument("--strip-debug-ops", action="store_false") 12 | parser.add_argument("--skip-op-check", action="store_true") 13 | parser.add_argument("--quantize", choices=["float16", "uint8", "uint16"], default=None) 14 | parser.add_argument("--experiments", action="store_true", help="enable experimental features") 15 | args = parser.parse_args() 16 | # fmt: on 17 | 18 | tensorflowjs.converters.convert_tf_saved_model( 19 | args.saved_model_path, 20 | args.output_path, 21 | quantization_dtype_map={args.quantize: "*"} if args.quantize else None, 22 | signature_def="model_inference", 23 | strip_debug_ops=args.strip_debug_ops, 24 | control_flow_v2=args.control_flow_v2, 25 | skip_op_check=args.skip_op_check, 26 | experiments=args.experiments, 27 | ) 28 | -------------------------------------------------------------------------------- /scripts/make_vocab.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from collections import Counter 4 | 5 | from tqdm import tqdm 6 | 7 | parser = argparse.ArgumentParser() 8 | parser.add_argument("--input-dir", required=True, help="Directory path containing text files for making vocab") 9 | parser.add_argument("--vocab-path", default="vocab.txt", help="Path to save vocab") 10 | 11 | if __name__ == "__main__": 12 | args = parser.parse_args() 13 | 14 | files = [fname for fname in os.listdir(args.input_dir) if fname.endswith(".txt")] 15 | print(f"Found {len(files)} files") 16 | 17 | counter = Counter() 18 | for file_name in tqdm(files): 19 | with open(os.path.join(args.input_dir, file_name)) as f: 20 | for line in f: 21 | counter.update(line.strip()) 22 | print("Counted all files") 23 | 24 | characters = ["", ""] + [char for char, count in counter.most_common()] 25 | 26 | with open(args.vocab_path, "w") as fout: 27 | for char in characters: 28 | fout.write(char + "\n") 29 | print(f"Saved to {args.vocab_path}") 30 | -------------------------------------------------------------------------------- /scripts/train_quickspacer.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import glob 3 | import json 4 | import os 5 | import shutil 6 | import sys 7 | 8 | import tensorflow as tf 9 | import tensorflow_addons as tfa 10 | 11 | from quickspacer import model 12 | from quickspacer.constant import DEFAULT_VOCAB_PATH 13 | from quickspacer.data import get_dataset 14 | from quickspacer.utils import f1_loss, f1_score, learning_rate_scheduler 15 | 16 | if __name__ == "__main__": 17 | # fmt: off 18 | parser = argparse.ArgumentParser() 19 | parser.add_argument("--model-name", type=str, default="ConvSpacer1") 20 | parser.add_argument("--model-config-file", type=str, default="configs/conv1-spacer-config.json") 21 | parser.add_argument("--pretrained-model-path", type=str, default=None) 22 | parser.add_argument("--min-space-remove-rate", type=float, default=0.4) 23 | parser.add_argument("--max-space-remove-rate", type=float, default=1.0) 24 | parser.add_argument("--shuffle-buffer-size", type=int, default=100000) 25 | parser.add_argument("--vocab-file-path", type=str, default=DEFAULT_VOCAB_PATH, help="vocab file path") 26 | parser.add_argument("--num-parallel-reads", type=int, default=4) 27 | parser.add_argument("--num-parallel-calls", type=int, default=4) 28 | parser.add_argument("--output-path", default="quickspacer_checkpoints/") 29 | parser.add_argument("--dataset-file-path", required=True, help="a text file or multiple files ex) *.txt") 30 | parser.add_argument("--loss", type=str, choices=["f1", "bce"], default="bce", help="loss function for training") 31 | parser.add_argument("--epochs", type=int, default=100) 32 | parser.add_argument("--steps-per-epoch", type=int, default=None) 33 | parser.add_argument("--learning-rate", type=float, default=2e-3) 34 | parser.add_argument("--weight-decay", type=float, default=0) 35 | parser.add_argument("--min-learning-rate", type=float, default=1e-8) 36 | parser.add_argument("--batch-size", type=int, default=8192) 37 | parser.add_argument("--val-batch-size", type=int, default=8192) 38 | parser.add_argument("--num-val-batch", type=int, default=30000) 39 | parser.add_argument("--tensorboard-update-freq", type=int, default=100) 40 | parser.add_argument("--disable-mixed-precision", action="store_false", dest="mixed_precision", help="Use mixed precision FP16") 41 | args = parser.parse_args() 42 | # fmt: on 43 | 44 | if args.mixed_precision: 45 | policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16") 46 | tf.keras.mixed_precision.experimental.set_policy(policy) 47 | print("Use Mixed Precision FP16") 48 | 49 | # Copy config file 50 | os.makedirs(args.output_path) 51 | with open(os.path.join(args.output_path, "configs.txt"), "w") as fout: 52 | for k, v in vars(args).items(): 53 | fout.write(f"{k}: {v}\n") 54 | shutil.copy(args.model_config_file, args.output_path) 55 | 56 | # Construct Dataset 57 | dataset_files = glob.glob(args.dataset_file_path) 58 | if not dataset_files: 59 | print("[Error] Dataset path is not valid!", file=sys.stderr) 60 | sys.exit(1) 61 | 62 | dataset = get_dataset( 63 | dataset_files, 64 | args.min_space_remove_rate, 65 | args.max_space_remove_rate, 66 | args.num_parallel_reads, 67 | args.num_parallel_calls, 68 | args.vocab_file_path, 69 | ).shuffle(args.shuffle_buffer_size) 70 | train_dataset = dataset.skip(args.num_val_batch).padded_batch(args.batch_size) 71 | valid_dataset = dataset.take(args.num_val_batch).padded_batch(max(args.batch_size, args.val_batch_size)) 72 | 73 | if args.steps_per_epoch: 74 | train_dataset.repeat() 75 | print("Repeat dataset") 76 | 77 | # Model Initialize 78 | with open(args.model_config_file) as f: 79 | model = getattr(model, args.model_name)(**json.load(f)) 80 | 81 | # Load pretrained model 82 | if args.pretrained_model_path: 83 | model.load_weights(args.pretrained_model_path) 84 | print("Loaded weights of model") 85 | 86 | # Model Compile 87 | model.compile( 88 | optimizer=tfa.optimizers.AdamW(args.weight_decay, args.learning_rate), 89 | loss=tf.keras.losses.BinaryCrossentropy() if args.loss == "bce" else f1_loss, 90 | metrics=[ 91 | tf.keras.metrics.BinaryAccuracy(), 92 | tf.keras.metrics.Precision(0.5, name="precision"), 93 | tf.keras.metrics.Recall(0.5, name="recall"), 94 | f1_score, 95 | ], 96 | ) 97 | 98 | # Training 99 | model.fit( 100 | train_dataset, 101 | validation_data=valid_dataset, 102 | epochs=args.epochs, 103 | steps_per_epoch=args.steps_per_epoch, 104 | callbacks=[ 105 | tf.keras.callbacks.ModelCheckpoint( 106 | os.path.join(args.output_path, "models", "spacer-{epoch}epoch-{val_f1_score:.4f}f1.ckpt"), 107 | save_weights_only=True, 108 | verbose=1, 109 | ), 110 | tf.keras.callbacks.TensorBoard( 111 | os.path.join(args.output_path, "logs"), update_freq=args.tensorboard_update_freq 112 | ), 113 | tf.keras.callbacks.LearningRateScheduler( 114 | learning_rate_scheduler(args.epochs, args.learning_rate, args.min_learning_rate), verbose=1 115 | ), 116 | ], 117 | ) 118 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import find_packages, setup 2 | 3 | 4 | with open("README.md") as f: 5 | long_description = f.read() 6 | 7 | setup( 8 | name="quickspacer", 9 | version="1.0.4", 10 | description="Korean spacing correction model that aims for fast speed and moderate accuracy.", 11 | long_description=long_description, 12 | long_description_content_type="text/markdown", 13 | python_requires=">=3.6", 14 | install_requires=["tensorflow>=2.1"], 15 | url="https://github.com/cosmoquester/quickspacer.git", 16 | author="Park Sangjun", 17 | keywords=["spacer", "korean"], 18 | classifiers=[ 19 | "Development Status :: 4 - Beta", 20 | "Natural Language :: Korean", 21 | "Programming Language :: Python :: 3", 22 | "Topic :: Scientific/Engineering :: Artificial Intelligence", 23 | "Topic :: Software Development :: Libraries :: Python Modules", 24 | "Topic :: Text Processing", 25 | ], 26 | packages=find_packages(exclude=["tests"]), 27 | package_data={ 28 | "quickspacer": [ 29 | "resources/vocab.txt", 30 | "resources/default_saved_model/*/*", 31 | "resources/default_saved_model/*/*/*", 32 | ] 33 | }, 34 | ) 35 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cosmoquester/quickspacer/f7292ee42227d68c7432fd929360f1d6664124bf/tests/__init__.py -------------------------------------------------------------------------------- /tests/test_constant.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import pytest 4 | import tensorflow as tf 5 | 6 | from quickspacer.constant import ( 7 | DEFAULT_MODEL_PATH, 8 | DEFAULT_OOV_INDEX, 9 | DEFAULT_SPACE_INDEX, 10 | DEFAULT_VOCAB_PATH, 11 | DEFAULT_VOCAB_SIZE, 12 | RESOURCE_PATH, 13 | ) 14 | from quickspacer.data import load_vocab 15 | 16 | 17 | def test_exist_files(): 18 | assert os.path.exists(RESOURCE_PATH) 19 | assert os.path.exists(DEFAULT_VOCAB_PATH) 20 | assert os.path.exists(DEFAULT_MODEL_PATH) 21 | 22 | 23 | def test_vocab(): 24 | vocab = load_vocab(DEFAULT_VOCAB_PATH, DEFAULT_OOV_INDEX) 25 | assert tf.cast(vocab.lookup(tf.constant(" ")), tf.int32) == DEFAULT_SPACE_INDEX 26 | assert tf.cast(vocab.lookup(tf.constant("이건절대vocab에없는말")), tf.int32) == DEFAULT_OOV_INDEX 27 | assert vocab.size() == DEFAULT_VOCAB_SIZE 28 | -------------------------------------------------------------------------------- /tests/test_data.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import tensorflow as tf 3 | 4 | from quickspacer.constant import DEFAULT_SPACE_INDEX 5 | from quickspacer.data import sentence_to_dataset 6 | 7 | 8 | @pytest.mark.parametrize( 9 | "token_ords,remove_rate", 10 | [ 11 | (tf.strings.unicode_decode("안 녕 하 세 요", "UTF-8"), 0.5), 12 | (tf.strings.unicode_decode("그만 좀 하자", "UTF-8"), 0.1), 13 | (tf.strings.unicode_decode("나는 테스트 코드 더 안 짜고 싶다구!", "UTF-8"), 0.2), 14 | (tf.strings.unicode_decode("으아아 아 아 아ㅏ 아 제바아아알", "UTF-8"), 0.3), 15 | ], 16 | ) 17 | def test_sentence_to_dataset(token_ords, remove_rate): 18 | num_space = tf.math.count_nonzero(token_ords == DEFAULT_SPACE_INDEX) 19 | num_removed = tf.cast(tf.math.ceil(tf.cast(num_space, tf.float32) * remove_rate), tf.int32) 20 | sentence_data, labels = sentence_to_dataset(token_ords, remove_rate, remove_rate) 21 | 22 | tf.debugging.assert_equal(tf.size(token_ords) - tf.size(sentence_data), num_removed) 23 | tf.debugging.assert_equal( 24 | num_space - tf.math.count_nonzero(sentence_data == DEFAULT_SPACE_INDEX), tf.cast(num_removed, tf.int64) 25 | ) 26 | 27 | restored_data = [] 28 | for token, label in zip(sentence_data, labels): 29 | restored_data.append(token) 30 | if label: 31 | restored_data.append(DEFAULT_SPACE_INDEX) 32 | 33 | tf.debugging.assert_equal( 34 | token_ords, 35 | tf.concat(restored_data, axis=0), 36 | ) 37 | -------------------------------------------------------------------------------- /tests/test_spacer.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | 3 | from quickspacer import Spacer 4 | 5 | 6 | @pytest.fixture 7 | def model1(): 8 | return Spacer(level=1) 9 | 10 | 11 | @pytest.fixture 12 | def model2(): 13 | return Spacer(level=2) 14 | 15 | 16 | def test_batch_space(model1): 17 | texts = ["안녕하세요", "이것좀띄워보게나자네분위기도좀띄우고말야", "지금제말이무슨말인지아시겠죠?", "AI인공지능만세!!!"] 18 | assert model1.space(texts) == model1.space(texts, batch_size=2) 19 | 20 | 21 | def test_empty_space(model2): 22 | assert model2.space([]) == [] 23 | 24 | 25 | def test_invalid_level(): 26 | with pytest.raises(AssertionError): 27 | Spacer(level=4) 28 | with pytest.raises(Exception): 29 | Spacer(saved_model_dir="") 30 | -------------------------------------------------------------------------------- /tests/test_utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import pytest 4 | import tensorflow as tf 5 | 6 | from quickspacer.constant import ( 7 | DEFAULT_MODEL_PATH, 8 | DEFAULT_OOV_INDEX, 9 | DEFAULT_SPACE_INDEX, 10 | DEFAULT_VOCAB_PATH, 11 | DEFAULT_VOCAB_SIZE, 12 | RESOURCE_PATH, 13 | ) 14 | from quickspacer.data import load_vocab 15 | 16 | 17 | def test_exist_files(): 18 | assert os.path.exists(RESOURCE_PATH) 19 | assert os.path.exists(DEFAULT_VOCAB_PATH) 20 | assert os.path.exists(DEFAULT_MODEL_PATH) 21 | 22 | 23 | def test_vocab(): 24 | vocab = load_vocab(DEFAULT_VOCAB_PATH, DEFAULT_OOV_INDEX) 25 | assert tf.cast(vocab.lookup(tf.constant(" ")), tf.int32) == DEFAULT_SPACE_INDEX 26 | assert tf.cast(vocab.lookup(tf.constant("이건절대vocab에없는말")), tf.int32) == DEFAULT_OOV_INDEX 27 | assert vocab.size() == DEFAULT_VOCAB_SIZE 28 | -------------------------------------------------------------------------------- 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