config.yaml

name: "KRED"
n_gpu: 1
data_loader:
    batch_size: 128
    shuffle: true
    num_workers: 1

data:
  train_news : "/datasets/mind_train/news.tsv"
  train_behavior : "/datasets/mind_train/behaviors.tsv"
  valid_news : "/datasets/mind_val/news.tsv"
  valid_behavior : "/datasets/mind_val/behaviors.tsv"
  knowledge_graph : "/datasets/mind_kg/triple2id.txt"
  entity_embedding : "/datasets/mind_kg/entity2vecd100.vec"
  num_entity_embedding: 3275150
  relation_embedding : "/datasets/mind_kg/relation2vecd100.vec"
  entity_index : "/datasets/mind_kg/entity2id.txt"
  relation_index : "/datasets/mind_kg/realtion2id.txt"
  sparse_adj_entity : "/datasets/mind_kg/entity_adj.npy"
  sparse_adj_relation : "/datasets/mind_kg/relation_adj.npy"
  mind_data : "/notebooks/data_dict_1671893708429.pkl"

model:
  document_embedding_dim: 768
  embedding_dim: 100
  news_entity_num: 20
  entity_neighbor_num: 20
  entity_embedding_dim: 100
  layer_dim: 128
  user_his_num: 20


trainer:
  training_type: "single_task" # single_task or multi-task
  task: "user2item" # task should be within: user2item, item2item, vert_classify, pop_predict
  epochs: 20
  smooth_lamda: 10
  train_neg_num: 4
  save_dir: "./out/saved/"
  save_period: 1
  early_stop: 3
  monitor: "val_auc"
  verbosity: 2

optimizer:
  type: "Adam"
  lr: 0.00002
  weight_decay: 0.000001
  amsgrad: true