DeBertaV3

<pre>A tutorial for implementing the DeBERTaV3 model On AWS<br><br>1. Introduction<br>This project implements a Machine Learning Pipeline with DeBERTaV3.<br><br>2. Code files<br>Here, we introduce main files for the ML pipeline of DeBERTaV3.<br>(a) sagemaker-pa-mlops-modelbuild/codebuild-buildspec.yml: it builds the ML continue-integrate pipeline with scripts in the sagemaker-pa-mlops-modelbuild folder.<br>(b) sagemaker-pa-mlops-modelbuild/pipelines: it builds nd register the ML pipeline.<br>(c) sagemaker-pa-mlops-modelbuild/pipelines/run_pipeline.py: it builds nd register the ML pipeline.<br>(d) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/train.py: it trains the DeBERTaV3 model.<br>(e) sagemaker-pa-mlops-modelbuild/pipelines/DeBERTaV3/evaluate.py: it evaluates the DeBERTaV3 model to report accuracy and f1-score.<br><br>3. Dataset and preprocess files<br>We provided test datasets for running the proposed MCGCN model, which contains one-day traffic speed, spatial context, and temporal context datasets in the folder “./raw_data/speed_origin_data/”. The detail of each file and its formats are as follows:<br>(a) Data-preprocess/CityofYarra_Verification_Data.xlsx: City of Yarra Dataset.<br>(b) Data-preprocess/Whitehorse_classification.xlsx: City of Whitehorse Dataset.<br>(c) Data-preprocess/high priority.csv: Planning applications with high priority Dataset.<br>(a) Data-preprocess/Split.ipynb: It splits the dataset.</pre>