pred = [
data.reverse_tag_to_idx[get_instance(pred_tag[idx][idy])]
for idy in range(seq_len) if mask[idx][idy] != 0
]
pred_label.append(pred)
return pred_label
seed_num = 123
random.seed(seed_num)
torch.manual_seed(seed_num)
np.random.seed(seed_num)
if __name__ == '__main__':
data = Data()
data.load('./data/PoSTagger.data')
predict_config_path = './predict.config'
data.readConfig(predict_config_path)
printParameterSummary(data)
predict_instances = getDataLoader(data.infer_path, data)
device = torch.device("cuda:" +
data.GPU if torch.cuda.is_available() else "cpu")
model = SequenceModel(data)
model = torch.load(data.model_save_path)
model.eval()
words = pd.read_csv(data.infer_path,
header=None,
