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디지털 산업혁신 빅데이터 플랫폼 공모전

[분석] 사업목적을 활용한 업종코드 예측

대회 기간 수행내용 결과
예선 21.04.09 ~ 21.05.23 사업목적을 활용한 업종코드 예측 본선 진출
본선 21.06.12 프레젠테이션 우수상(2위)

📌 Techniques

NLP


📌 Process

1. Tokenizer - Mecab

  • 다음과 같이 git-clone하여 사용하실 수 있습니다.

    %cd /content/
    !git clone https://github.com/SOMJANG/Mecab-ko-for-Google-Colab.git
    %cd Mecab-ko-for-Google-Colab
    ! bash install_mecab-ko_on_colab190912.sh
    

2. Embedding - Fasttext

  • 위키피디아 pretrain model 사용하였습니다.

3. Modeling - BILSTM

  • 단어 구성에 주목할 수 있는 양방향 순환신경망 고려한 모델을 적용하였습니다.

    def build_model():
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128, return_sequences=True), input_shape=(None, n_features)))
      model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128)))
      model.add(tf.keras.layers.Dense(100, kernel_initializer= 'he_normal', activation='relu'))
      model.add(tf.keras.layers.Dropout(0.3))
      model.add(tf.keras.layers.Dense(19, activation='softmax'))    
      return model      
    

📌 실행 환경

  • Tensorflow 2.4.0 버전에서 작성되었습니다.
  • colab pro 환경에서 작성되었습니다.

ked-project's People

Contributors

daeunni avatar bominkm avatar

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