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config.py
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config.py
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from torch import cuda
data_dir = "/usr/local/courses/lt2316-h19/a2/hansard/scrapedxml/debates/"
CSV_FILENAME = "parliamentary_speech_data.csv"
CSV_FILENAME_TRAIN = 'parliamentary_speech_data_train.csv'
CSV_FILENAME_TEST = 'parliamentary_speech_data_test.csv'
PATH_TO_PRETRAINED_EMBEDDINGS = '/scratch/GoogleNews-vectors-negative300.bin.gz'
BOUNDARY_TO_INT_MAPPING = {
'[SAME]': 0,
'[CHANGE]': 1
}
RNN_BATCH_SIZE = 256
RNN_NUM_EPOCHS = 100
RNN_HIDDEN_SIZE = 300
RNN_NUM_RECORDS = 100000
RNN_SAME_ADDITIONAL_RECORDS = 5000
RNN_LEARNING_RATE = 0.0005
USE_ATTENTION = True
EQUALIZE_CLASS_COUNTS = True
RNN_MODEL1 = 'model_1.pkl'
RNN_MODEL2 = 'model_2.pkl'
RNN_CLASSIFIER = 'classifier.pkl'
RNN_EQ_MODEL1 = 'model_eq_1.pkl'
RNN_EQ_MODEL2 = 'model_eq_2.pkl'
RNN_EQ_CLASSIFIER = 'classifier_eq.pkl'
RNN_ATTENTION_MODEL1 = 'model_attention_1.pkl'
RNN_ATTENTION_MODEL2 = 'model_attention_2.pkl'
RNN_ATTENTION_CLASSIFIER = 'classifier_attention.pkl'
RNN_EQ_ATTENTION_MODEL1 = 'model_eq_attention_1.pkl'
RNN_EQ_ATTENTION_MODEL2 = 'model_eq_attention_2.pkl'
RNN_EQ_ATTENTION_CLASSIFIER = 'classifier_eq_attention.pkl'
DEVICE = "cuda:1" if cuda.is_available() else "cpu"
UNKNOWN_WORD_VECTOR = [-0.505834698677063, 0.8371995091438293, -0.3907436430454254, 1.2388476133346558, 0.39628866314888, 0.659585177898407, 0.2056134194135666, -1.2870607376098633, 0.22113212943077087, -1.330844759941101, 0.8694334030151367, 0.41195404529571533, -0.17144230008125305, -0.6751164197921753, 2.0545945167541504, 1.5696337223052979, 0.8888554573059082, 0.6383231282234192, 0.34052929282188416, -1.718234896659851, 1.7102441787719727, -0.74288010597229, 1.2348617315292358, 0.20869804918766022, 0.010648425668478012, 0.7828048467636108, 0.4952743649482727, 0.7345136404037476, 0.7647744417190552, -0.08482004702091217, -1.0111994743347168, -0.8100836873054504, -0.24033965170383453, -1.593235731124878, -1.7683085203170776, -0.18870864808559418, 0.15959683060646057, 0.06499271094799042, 0.14582936465740204, 0.4639718234539032, 1.082289218902588, -0.4073246121406555, 1.0503937005996704, 0.8217169046401978, 0.04474393650889397, 1.134543538093567, -1.8783674240112305, 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0.1850944608449936, 1.288645625114441, -0.4520632028579712, 1.0726860761642456, 0.769446849822998, 1.165770173072815, -0.6193650364875793, 0.19318526983261108, 0.20301254093647003, -1.4767383337020874, -0.7551678419113159, 1.3037528991699219, -0.3005061745643616]
# for BERT
BERT_BATCH_SIZE = 16
BERT_NUM_EPOCHS = 4
BERT_MAX_SENT_LEN = 256
BERT_NUM_RECORDS = 1000000
BERT_MODEL_FILE = "pytorch_model.bin"
BERT_CONFIG_FILE = "config.json"
BERT_VOCAB_FILE = "vocab.txt"
BERT_TAR_FILE = "bert_model.tar.gz"