backbone_config_path: /home/models/exp478
best_model_path: /home/models/exp478/models/fold_0_10_best.pth
checkpoint_path: /home/models/exp478/chkp/fold_0_10_chkp.pth
config_path: /home/models/exp478/config.yaml
criterion:
  criterion_type: BCEWithLogitsLoss
  mcrmse_loss:
    weights:
    - 0.5
    - 0.5
  mse_loss:
    reduction: mean
  rmse_loss:
    eps: 1.0e-09
    reduction: mean
  smooth_l1_loss:
    beta: 0.1
    reduction: mean
data_dir: /home/data
dataset:
  bucket_batch_sampler:
    bucket_size: 400
    noise_factor: 0.2
  folds: true
  labels:
  - generated
  max_length: 1024
  sampler_type: StratifiedBatchSampler
  train_batch_size: 6
  train_sources:
  - daigt
  - persuade
  - persuade_gpt
  - persuade_humanized_1
  - persuade_gpt_patially_rewritten
  - persuade_gpt_patially_rewritten_05
  - persuade_humanized_easy_1
  - daigt_gpt_patially_rewritten
  - llama-mistral-partially-r
  - moth
  - books
  - neural-chat-7b
  - nbroad
  valid_batch_size: 6
  valid_sources:
  - none
debug: false
exp_name: exp478_seed10
external_dir: /home/data/external
fold: 0
interim_dir: /home/data/interim
log_path: /home/models/exp478/logs/fold-0.log
logger:
  job_type: training
  project: DAIGT-AIE
  train_print_frequency: 100
  use_wandb: true
  valid_print_frequency: 100
model:
  architecture_type: CustomModel
  attention_dropout: 0.1
  backbone_type: microsoft/deberta-v3-large
  dropout: 0.05
  freeze_embeddings: true
  freeze_n_layers: 20
  gem_pooling:
    eps: 1.0e-06
    p: 3
  gradient_checkpointing: false
  load_embeddings: true
  load_head: false
  load_n_layers: 24
  load_parts: true
  pooling_type: MeanPooling
  reinitialize_n_layers: 0
  state_from_model: exp475
models_dir: /home/models
optimizer:
  beta1: 0.9
  beta2: 0.999
  decoder_lr: 2.0e-05
  embeddings_lr: 2.0e-05
  encoder_lr: 2.0e-05
  eps: 1.0e-06
  group_lr_multiplier: 1
  n_groups: 1
  weight_decay: 0.01
processed_dir: /home/data/processed
raw_dir: /home/data/raw
run_dir: /home/models/exp478
run_id: exp478_seed10_fold0
run_name: exp478_seed10_fold0
scheduler:
  cosine_schedule_with_warmup:
    n_cycles: 0.5
    n_warmup_steps: 0
  type: cosine_schedule_with_warmup
seed: 10
tokenizer: null
tokenizer_path: /home/models/exp478/tokenizer
training:
  apex: true
  epochs: 1
  evaluate_n_times_per_epoch: 1
  gradient_accumulation_steps: 1
  max_grad_norm: 1000