# Job_skill_match

By using the following command, you can run the code.

    python3 main.py --run-type both --output-folder result --token-raw-truth data/large_dataset/ex_list.xlsx --token-raw-txt-folder data/large_dataset/ads_txt_fltr 

I modulized this code into two components: tokenizer + statistics, and LSTM model.
Thus, you can run the following commands to check individual components.

    python3 main.py --run-type tokenizer --output-folder result --token-raw-truth data/large_dataset/ex_list.xlsx --token-raw-txt-folder data/large_dataset/ads_txt_fltr 

    python3 main.py --run-type lauguage_model --output-folder result --lstm-token result/tokenization_result/subword_token.npy --lstm-truth result/tokenization_result/subword_truth.npy



Also, for educational purpose, I also prepared notebook files for tokenizer and LSTM model.



training
python3 main.py --run-type both --output-folder result --token-raw-truth data/large_dataset/ex_list.xlsx --token-raw-txt-folder data/large_dataset/ads_txt_fltr 


prediction
python3 main.py --run-type predict --token-raw-txt-folder input --saved-labels data/test_model/label.pkl --saved-model data/test_model/model.pt --saved-vocab data/test_model/vocab.pkl --output-folder result

 