args.yaml

model:
  word_emb_dim:
    val: 128
    meaning: "The dimension in which a word is embedded."
  grnn_hidden_dim:
    val: 128
    meaning: "The number of hidden nodes in the GRNN layer."
  bigru_num:
    val: 2
    meaning: "The number of bi_gru layers in the network."
  init_checkpoint:
    val: ""
    meaning: "Path to init model"
  inference_save_dir:
    val: ""
    meaning: "Path to save inference model"

train:
  random_seed:
    val: 0
    meaning: "Random seed for training"
  print_steps:
    val: 1
    meaning: "Print the result per xxx batch of training"
  save_steps:
    val: 10
    meaning: "Save the model once per xxxx batch of training"
  validation_steps:
    val: 10
    meaning: "Do the validation once per xxxx batch of training"
  batch_size:
    val: 300
    meaning: "The number of sequences contained in a mini-batch"
  epoch:
    val: 10
    meaning: "Corpus iteration num"
  use_cuda:
    val: False
    meaning: "If set, use GPU for training."
  traindata_shuffle_buffer:
    val: 20000
    meaning: "The buffer size used in shuffle the training data."
  base_learning_rate:
    val: 0.001
    meaning: "The basic learning rate that affects the entire network."
  emb_learning_rate:
    val: 2
    meaning: "The real learning rate of the embedding layer will be (emb_learning_rate * base_learning_rate)."
  crf_learning_rate:
    val: 0.2
    meaning: "The real learning rate of the embedding layer will be (crf_learning_rate * base_learning_rate)."
  enable_ce:
    val: false
    meaning: 'If set, run the task with continuous evaluation logs.'
  cpu_num:
    val: 10
    meaning: "The number of cpu used to train model, this argument wouldn't be valid if use_cuda=true"

data:
  word_dict_path:
    val: "./conf/word.dic"
    meaning: "The path of the word dictionary."
  label_dict_path:
    val: "./conf/tag.dic"
    meaning: "The path of the label dictionary."
  word_rep_dict_path:
    val: "./conf/q2b.dic"
    meaning: "The path of the word replacement Dictionary."
  train_data:
    val: "./data/train.tsv"
    meaning: "The folder where the training data is located."
  test_data:
    val: "./data/test.tsv"
    meaning: "The folder where the test data is located."
  infer_data:
    val: "./data/infer.tsv"
    meaning: "The folder where the infer data is located."
  model_save_dir:
    val: "./models"
    meaning: "The model will be saved in this path."