senti_debias.yaml

_target_: newsreclib.models.fair_rec.senti_debias_module.SentiDebiasModule

# discriminator
discriminator:
  _target_: newsreclib.models.fair_rec.senti_debias_module.Discriminator
  input_dim: 300
  hidden_dim: 256
  output_dim: 3

# generator
generator:
  _target_: newsreclib.models.fair_rec.senti_debias_module.Generator
  # training strategy
  late_fusion: False
  # news encoder
  dataset_attributes: ${data.dataset_attributes}
  attributes2encode: ["title"]
  use_plm: False
  # text encoder
  pretrained_embeddings_path: null
  plm_model: null
  frozen_layers: null
  embed_dim: 300
  num_heads: 15
  query_dim: 200
  dropout_probability: 0.2
  # sentiment encoder
  sentiment_encoder:
    _target_: newsreclib.models.components.encoders.news.aspect.SentimentEncoder
    num_sent_classes: 3
    sent_embed_dim: 256
    sent_output_dim: 300

# outputs
outputs:
  train: ["preds", "targets", "cand_news_size"]
  val: ["preds", "targets", "cand_news_size"]
  test:
    [
      "preds",
      "targets",
      "cand_news_size",
      "hist_news_size",
      "target_categories",
      "target_sentiments",
      "hist_categories",
      "hist_sentiments",
      "user_ids",
      "cand_news_ids",
    ]

# loss coefficients
alpha_coefficient: 0.15
beta_coefficient: 10

# evaluation
top_k_list: [5, 10]
num_categ_classes: 18
num_sent_classes: 3
save_recs: False
recs_fpath: "${paths.output_dir}/recommendations.json"

optimizer: null

optimizer_generator:
  _target_: torch.optim.Adam
  _partial_: True
  lr: 0.00001

optimizer_discriminator:
  _target_: torch.optim.Adam
  _partial_: True
  lr: 0.00002

scheduler: null