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parameters.py
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parameters.py
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#pretrained_model = '/home/dzm44/Documents/projects/clinical-bert/clinical-bert-weights'
#code_graph_file = '/home/dzm44/Documents/data/icd_codes/code_graph_radiology_expanded.pkl'
pretrained_model = '/home/jered/Documents/projects/clinical-bert/clinical-bert-weights'
code_graph_file = '/home/jered/Documents/data/icd_codes/code_graph_radiology_expanded.pkl'
#data_dir = '/home/dzm44/Documents/data/Dataset_10-11-2019/preprocessed/reports_and_codes_expanded'
data_dir = '/home/jered/Documents/data/Dataset_10-11-2019/preprocessed/reports_and_codes_expanded'
#data_dir = '/home/jered/Documents/data/Dataset_10-11-2019/preprocessed/mini'
#data_dir = '/home/jered/Documents/data/mimic-iii-clinical-database-1.4/preprocessed/reports_and_codes_expanded'
#data_dir = '/home/jered/Documents/data/mimic-iii-clinical-database-1.4/preprocessed/mini'
val_file = 'val.data'
#val_file = 'test.data'
# training params
learning_rate_unfrozen = .00001
learning_rate_frozen = .001
batch_size = 8
epochs = 3
#epochs = 5
limit_rows_train = 10000
#limit_rows_train = None
limit_rows_val = 1000
#limit_rows_val = None
subbatches = 8
num_workers = 2
checkpoint_every = 10
copy_checkpoint_every = 300
val_every = 10
email_every = 30
expensive_val_every = 500
#expensive_val_every = None
# model params
sentences_per_checkpoint = 30
concatenate_code_embedding = True
# email params
smtp_server = 'smtp.gmail.com'
port = 465
sender_email = 'jeredspython@gmail.com'
receiver_email = 'jered.mcinerney@gmail.com'