option.py

import torch

class option(object):
    n_layers=6
    n_position = 160
    n_inner_vocab = 5000
    n_inner_layers = 3
    n_inner_position = 15
    d_word_vec = 512
    n_head = 8
    d_k = 64
    d_v = 64
    pad_idx = 2
    d_model = 512
    d_inner = 2048
    dropout = 0.1
    n_warmup_steps = 2000
    scale_emb = False
    switch_interval = 16
    cache_turn = 0
    context_max_len = 150
    r_max_len = 50
    r_beam_max_len = 30
    conv_max_len = 500
    profile_num = 1  
    state_num = 10
    state_num_redial = 20
    pretrain_state_num = 50
    all_topic_num = 20
    all_topic_num_redial = 40
    movie_path_len = 3
    tag_num = 3
    preference_num = 1  
    topic_num = 2
    action_num = 10  
    action_num_redial = 1
    relation_num = 150
    movie_num = 200
    state_token = 40
    scale_prj = True
    epoch = 100
    task = "meddg"
    dataset_file = "dataset/{dataset}.zip"
    topic_file = "dataset/TG-ReDial/topic.txt"
    topic_movie_file = "dataset/TG-ReDial/tpmv.txt"
    vocab_file = "dataset/TG-ReDial/tpvocab.txt"
    vocab_movie_file = "dataset/TG-ReDial/tpmvvocab.txt"
    no_action_super = None
    max_patience = 20
    log_loss_interval = 100
    gradient_stack = 8
    decay_interval = 10000
    decay_rate = 0.9
    lr = 1e-5
    valid_eval_interval = 10000
    test_eval_interval = 10000
    force_ckpt_dump = True
    sub_gen_lambda = 0.01
    s_copy_lambda = 1
    a_copy_lambda = 1
    copy_lambda_mini = 0.1
    copy_lambda_decay_steps = 10000
    copy_lambda_decay_value = 1.0
    init_tau = 1.0
    tau_mini = 0.1
    tau_decay_total_steps = 5000
    tau_decay_rate = 0.5
    beam_width = 1
    wo_l = False
    wo_m = False
    wo_entropy_restrain = False
    wo_repeat_penalty = False
    wo_rl = False
    super_only = False
    hungary = False
    super_rate = 0.0
    super_epoch = 5
    batch_size = 16
    reg_lambda = 5e-3
    BOS_CONTEXT = "[s_context]"
    EOS_CONTEXT = "[/s_context]"
    BOS_RESPONSE = "[s_response>]"
    EOS_RESPONSE = "[/s_response]"
    BOS_ACTION = "[s_action]"
    EOS_ACTION = "[/s_action]"
    PAD_WORD = "[PAD]"
    SENTENCE_SPLITER = "[sent]"
    TOPIC_SPLITER = "[unused2]"
    UNK_WORD = "[UNK]"
    BOS_PRE = "[s_preference]"
    EOS_PRE = "[/s_preference]"
    BOS_PRO = "[s_profile]"
    EOS_PRO = "[/s_profile]"