text: USE_CUDA = True
  MODEL_CHECKPOINT_FOLDER = "checkpoints/"
  TEMPORARY_CHECKPOINTS_PATH = "temporary_checkpoints/"
  
  # TODO Refactor with ArgumentParser
  
  # -- Trainer --
  MAX_SENTENCE_LENGTH = 56
  MAX_GRADIENT = 10
  
  # -- Word embedding --
  WORD_EMBEDDING_SIZE = 300
  
  # -- Meta features --
  META_FEATURES_DROPOUT = 0.2
  
  # -- Char CNN --
  MAX_WORD_LENGTH = 11
  CHAR_VOCAB_SIZE = 100
  CHAR_EMBEDDING_SIZE = 10
  CHAR_EMBEDDING_FEATURE_MAP_NUMS = [10, 80, 100, 80, 80,]
  CHAR_EMBEDDING_WINDOW_SIZES = [1, 2, 3, 4, 5]
  CHAR_CNN_OUT_SIZE = sum(CHAR_EMBEDDING_FEATURE_MAP_NUMS)
  
  # -- Compare layer --
  COMPARE_LAYER_DROPOUT = 0.2
  
  # -- CARNN --
  CARNN_RNN_SIZE = 76
  CARNN_AGGREATION_DROPOUT = 0.2
  CARNN_COMPARE_LAYER_OUTSIZE = 8
  CARNN_COMPARE_LAYER_HIDDEN_SIZE = 288
  CARNN_CONCAT_DROPOUT = 0.5
  CARNN_MLP_HIDDEN_SIZE = 256
  
  EMBEDDING_DROPOUT = 0.5
  CONCAT_DROPOUT = 0.7
  
  # -- DACNN --
  DACNN_FUSION_GATE = True
  DACNN_FUSION_ACTIVATION = 'relu'
  DACNN_AUG_LAYERS = 3
  DACNN_AUG_SIZE = 48
  DACNN_AUG_ACTIVATION = 'relu'
  DACNN_CONCAT_DROPOUT = 0.5
  
  DACNN_NORM = True
  DACNN_WITH_BOTTLENECK = True
  DACNN_ALL_FEATURES = False
  DACNN_HIGHWAY_LAYERS = 2
  
  META_FEATURES = ['bm25_q1_to_q2', 'bm25_q2_to_q1', 'weighted_cosine_sim',
         'len_word_max', 'len_word_min', 'len_char_max', 'len_char_min',
         'word_length_diff', 'char_length_diff', 'len_diff_remove_stopwords',
         'first_word_the_same', 'word_match', 'tfidf_word_match', 'shared_count', 'bigram_corr', 'trigram_corr',
         'word_match_no_stopwords', 'unique_word_ratio', 'cosine_sim',
         'manhattan_dis', 'eucledian_dis', 'jaccard_dis', 'minkowsk_dis',
         'fuzzy_ratio', 'fuzzy_set_ratio', 'fuzzy_partial_ratio',
         'fuzzy_token_sort_ratio', 'fuzzy_qratio', 'fuzzy_WRatio',
         'longest_substr_ratio', 'cómo_both', 'simhash_distance', 'simhash_distance_2gram',
         'simhash_distance_3gram', 'simhash_distance_ch_2gram',
         'simhash_distance_ch_3gram', 'raw_wmd', 'word2vec_jaccard_distance',
         'freq_based_word2vec_cosine_distance', 'freq_based_word2vec_jaccard_distance',
         'lda_balanced_euclidean_distance', 'lsi_cosine_distance',
         'lsi_jaccard_distance', 'jellyfish_jaro_winkler_distance', 'smith_waterman_distance'
  ]