if __name__ == "__main__":
model_name = "TransformerText" # TextRNN, TextCNN， lSTMATT, TextRCNN, TransformerText
data_dir = "/search/hadoop02/suanfa/songyingxin/data/SST-2"
cache_dir = ".cache/"
embedding_folder = "/search/hadoop02/suanfa/songyingxin/data/embedding/glove/"
model_dir = ".models/"
log_dir = ".log/"
if model_name == "TextCNN":
from TextCNN import args, TextCNN
elif model_name == "TextRNN":
from TextRNN import args, TextRNN
elif model_name == "LSTMATT":
from LSTM_ATT import args, LSTMATT
elif model_name == "TextRCNN":
from TextRCNN import args, TextRCNN
elif model_name == "TransformerText":
from TransformerText import args, TransformerText
main(
args.get_args(data_dir, cache_dir, embedding_folder, model_dir,
log_dir))
print('{}'.format(dev_report))
model.load_state_dict(torch.load('tut2-model.pt'))
test_loss, test_acc, test_report = evaluate(model, test_iterator,
criterion, config.output_dim)
print("-------------- Test -------------")
print(f'\t \t Loss: {test_loss: .3f} | Acc: {test_acc*100: .2f} %')
print('{}'.format(test_report))
if __name__ == "__main__":
model_name = "TextRNN" # TextRNN, TextCNN， lSTMATT, TextRCNN
data_dir = "/home/songyingxin/datasets/SST-2"
cache_dir = data_dir + "/cache/"
embedding_folder = "/home/songyingxin/datasets/WordEmbedding/"
if model_name == "TextCNN":
from TextCNN import args, TextCNN
main(args.get_args(data_dir, cache_dir, embedding_folder))
elif model_name == "TextRNN":
from TextRNN import args, TextRNN
main(args.get_args(data_dir, cache_dir, embedding_folder))
elif model_name == "LSTMATT":
from LSTM_ATT import args, LSTMATT
main(args.get_args(data_dir, cache_dir, embedding_folder))
elif model_name == "TextRCNN":
from TextRCNN import args, TextRCNN
main(args.get_args(data_dir, cache_dir, embedding_folder))