LSTMs are particularly effective for tasks involving sequential data, such as text classification, where the order and context of words are important. They can capture long-term dependencies and maintain relevant information over time, making them well-suited for processing and understanding textual data.
- constants
- config_enity
- artifact_enity
- components
- pipeline
- app.py
conda create -p hate python=3.10 -yconda init
conda activate D:\Text-classification\hateconda activate hatepip install -r requirements.txthttps://dl.google.com/dl/cloudsdk/channels/rapid/GoogleCloudSDKInstaller.exe
gcloud initpython app.py