/TFX_Pipeline

TFX를 활용한 LSTM모델의 Text Data 감성분석 Pipeline 제작 Project

Primary LanguageJupyter Notebook

Sentiment analysis model within TFX_Pipeline

RNN 모델의 Text Data 감성분석 Pipeline 제작 Project

TFX를 활용하여 텍스트데이터 감성분석을 수행하는 RNN 모델의 파이프라인을 구축하고 streamlit을 활용해 웹서비스화 해볼 것이다

Data

-Naver Sentiment movie corpus

To-do

박찬성님의 TFX Pipeline todo를 참고하였다.

  • Notebook to prepare input dataset in TFRecord format
  • Upload the input dataset into the GCS bucket
  • Implement and include RNN model in the pipeline
  • Implement Streamlit app template
  • Make a complete TFX pipeline with ExampleGen, SchemaGen, Resolver, Trainer, Evaluator, and Pusher components
  • Add necessary configurations to the configs.py
  • Add HFPusher component to the TFX pipeline
  • Replace SchemaGen with ImportSchemaGen for better TFRecords parsing capability
  • (Optional) Integrate Dataflow in ImportExampleGen to handle a large amount of dataset. This feature is included in the code as a reference, but it is not used after we switched the Sidewalk to PETS dataset.

References