3.6.1 <= python <= 3.8.x
torch == 1.5.1
scikit-learn == 1.0.2
tqdm == 4.63.0
pandas == 1.0.5
pytorch-crf == 0.7.2
python bin_classifier.py -conf conf/bin/cnn.json -save true
python bin_classifier.py -conf conf/bin/cnn_rnn.json -save true
python bin_classifier.py -conf conf/bin/rnn_cnn.json -save true
python bin_classifier.py -conf conf/bin/rnn.json -save true
python ner_classifier.py -conf conf/ner/cnn.json -save true
python ner_classifier.py -conf conf/ner/cnn_rnn.json -save true
python ner_classifier.py -conf conf/ner/rnn_cnn.json -save true
python ner_classifier.py -conf conf/ner/rnn.json -save true
python mt_classifier.py -conf conf/mt/cnn.json -save true
python mt_classifier.py -conf conf/mt/cnn_rnn.json -save true
python mt_classifier.py -conf conf/mt/rnn_cnn.json -save true
python mt_classifier.py -conf conf/mt/rnn.json -save true
All model results will be stored in ckpts
. bin
corresponds to the binary classifier,
ner
corresponds to the named entity recognition model, and mt
corresponds to the
multi-task model.
Running grid search will take an extremely long amount of time. Results for grid search
will be stored in results
. If the folder does not exist, run the script and the folder
will be created automatically.
python grid_search.py -task bin -arch cnn
python grid_search.py -task bin -arch cnn_rnn
python grid_search.py -task bin -arch rnn_cnn
python grid_search.py -task bin -arch rnn
python grid_search.py -task ner -arch cnn
python grid_search.py -task ner -arch cnn_rnn
python grid_search.py -task ner -arch rnn_cnn
python grid_search.py -task ner -arch rnn
python grid_search.py -task mt -arch cnn
python grid_search.py -task mt -arch cnn_rnn
python grid_search.py -task mt -arch rnn_cnn
python grid_search.py -task mt -arch rnn