config/reproduction/mix-atis/vanilla.yaml

base:
  name: "OpenSLUv1"
  multi_intent: true
  train: true
  test: true
  device: cuda
  seed: 42
  epoch_num: 100
  batch_size: 16
  ignore_index: -100

model_manager:
  load_dir: null
  save_dir: save/vanilla-mix-atis

evaluator:
  best_key: EMA
  eval_by_epoch: true
  # eval_step: 1800
  metric:
    - intent_acc
    - intent_f1
    - slot_f1
    - EMA

dataset:
  dataset_name: atis

tokenizer:
    _tokenizer_name_: word_tokenizer
    _padding_side_: right
    _align_mode_: fast
    add_special_tokens: false
    max_length: 512

optimizer:
  _model_target_: torch.optim.Adam
  _model_partial_: true
  lr: 0.001
  weight_decay: 1e-6

scheduler:
  _model_target_: transformers.get_scheduler
  _model_partial_: true
  name : "linear"
  num_warmup_steps: 0

model:
  _model_target_: model.OpenSLUModel

  encoder:
    _model_target_: model.encoder.AutoEncoder
    encoder_name: self-attention-lstm

    embedding:
      embedding_dim: 128
      dropout_rate: 0.4

    lstm:
      layer_num: 1
      bidirectional: true
      output_dim: 256
      dropout_rate: 0.4

    attention:
      hidden_dim: 1024
      output_dim: 128
      dropout_rate: 0.4
    output_dim: "{model.encoder.lstm.output_dim} + {model.encoder.attention.output_dim}"
    return_with_input: true
    return_sentence_level_hidden: true

  decoder:
    _model_target_: model.decoder.BaseDecoder

    intent_classifier:
      _model_target_: model.decoder.classifier.LinearClassifier
      mode: "intent"
      input_dim: "{model.encoder.output_dim}"
      loss_fn:
        _model_target_: torch.nn.BCEWithLogitsLoss
      use_multi: "{base.multi_intent}"
      multi_threshold: 0.5
      return_sentence_level: true
      ignore_index: "{base.ignore_index}"


    slot_classifier:
      _model_target_: model.decoder.classifier.LinearClassifier
      mode: "slot"
      input_dim: "{model.encoder.output_dim}"
      use_multi: false
      multi_threshold: 0.5
      ignore_index: "{base.ignore_index}"
      return_sentence_level: false