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.DS_Store
.DS_Store
 
 
Fine_Tuning_flan_t5.ipynb
Fine_Tuning_flan_t5.ipynb
 
 
Group_10_BART_Fine_Tuning_Model.ipynb
Group_10_BART_Fine_Tuning_Model.ipynb
 
 
Group_10_BART_Inferencing.ipynb
Group_10_BART_Inferencing.ipynb
 
 
Group_10_Fine_Tuning_Implementation_Random_Baseline.ipynb
Group_10_Fine_Tuning_Implementation_Random_Baseline.ipynb
 
 
QuestionAnswering.ipynb
QuestionAnswering.ipynb
 
 
README.md
README.md
 
 
Social Media Mining Final Presentation Group 10.pptx
Social Media Mining Final Presentation Group 10.pptx
 
 
Social Media Mining Final Report.pdf
Social Media Mining Final Report.pdf
 
 
fine_tuning_dataset_seed_1.csv
fine_tuning_dataset_seed_1.csv
 
 
fine_tuning_dataset_seed_2.csv
fine_tuning_dataset_seed_2.csv
 
 
fine_tuning_dataset_seed_3.csv
fine_tuning_dataset_seed_3.csv
 
 
fine_tuning_dataset_seed_4.csv
fine_tuning_dataset_seed_4.csv
 
 
fine_tuning_dataset_seed_5.csv
fine_tuning_dataset_seed_5.csv
 
 
main.py
main.py
 
 
postings.csv
postings.csv
 
 
postings.xlsx
postings.xlsx
 
 
scrapping.py
scrapping.py
 
 
software_engineering_postings_dataset.zip
software_engineering_postings_dataset.zip
 
 

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SocialMediaMiningFinalProject

The purpose of the project is identifying what are the most relevant skills a software engineer could use to help them on their job hunt. We broke our methodology into 4 distinct levels: 1) Level 0: Scrapper Implementation, 2) Level 1: Excel Macros For Postings, 3) Level 2: AI Model for Postings, 4) Level 3: Cross-model Analysis with Different NLP Models

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