run_all_glove

# runs with random embeddings initializations
python glove.py -em 50 -wn 10 -nm 10000 -lr 0.05
python glove.py -em 50 -wn 10 -nm 100000 -lr 0.05
python glove.py -em 50 -wn 5 -nm 10000 -lr 0.05
python glove.py -em 50 -wn 10 -nm 10000 -lr 0.1
python glove.py -em 100 -wn 10 -nm 10000 -lr 0.05
python glove.py -em 100 -wn 10 -nm 100000 -lr 0.05
python glove.py -em 100 -wn 5 -nm 10000 -lr 0.05
python glove.py -em 100 -wn 10 -nm 10000 -lr 0.1
python glove.py -em 100 -wn 5 -nm 10000 -lr 0.05 -ep 15

# runs with initializations with pretrained vectors
python glove.py -em 50 -wn 10 -nm 10000 -lr 0.05 -pt True
python glove.py -em 50 -wn 10 -nm 100000 -lr 0.05 -pt True
python glove.py -em 50 -wn 5 -nm 10000 -lr 0.05 -pt True
python glove.py -em 50 -wn 10 -nm 10000 -lr 0.1 -pt True
python glove.py -em 100 -wn 10 -nm 10000 -lr 0.05 -pt True
python glove.py -em 100 -wn 10 -nm 100000 -lr 0.05 -pt True
python glove.py -em 100 -wn 5 -nm 10000 -lr 0.05 -pt True
python glove.py -em 100 -wn 10 -nm 10000 -lr 0.1 -pt True
python glove.py -em 100 -wn 5 -nm 10000 -lr 0.05 -pt True -ep 15

# tests with logistic regression baseline
# path is:
# data/all_<embedding_size>_<num_words>_<num_epochs>_<learning_rate>_<window_size>_<initialize_with_pretrained_path>_embeddings.pkl
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_10000_2_0.05_10_False_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_100000_2_0.05_10_False_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_10000_2_0.05_5_False_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_10000_2_0.1_10_False_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_2_0.05_10_False_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_100000_2_0.05_10_False_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_2_0.05_5_False_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_2_0.1_10_False_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_15_0.05_5_False_embeddings.pkl -dims 100

python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_10000_2_0.05_10_True_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_100000_2_0.05_10_True_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_10000_2_0.05_5_True_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_50_10000_2_0.1_10_True_embeddings.pkl -dims 50
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_2_0.05_10_True_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_100000_2_0.05_10_True_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_2_0.05_5_True_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_2_0.1_10_True_embeddings.pkl -dims 100
python logistic_baseline_sklearn.py -dataset toxic -local data/all_100_10000_15_0.05_5_True_embeddings.pkl -dims 100

# run with RNNs
python rnn_tensorflow.py -embeds ours -dataset attack -cell gru -adapt_lr -sigmoid -nepochs 5 -attn -bd -max_length 150
python rnn_tensorflow.py -embeds ours -dataset toxic -cell gru -adapt_lr -sigmoid -nepochs 5 -attn -bd -max_length 150
python rnn_tensorflow.py -embeds ours -dataset attack -cell lstm -adapt_lr -sigmoid -nepochs 5 -attn -bd -max_length 150
python rnn_tensorflow.py -embeds ours -dataset toxic -cell lstm -adapt_lr -sigmoid -nepochs 5 -attn -bd -max_length 150