command:
- python3
- ${program}
- --overwrite_output_dir
- --freeze_feature_encoder
- --gradient_checkpointing
- --predict_with_generate
- --fp16
- --group_by_length
- --do_train
- --do_eval
- --load_best_model_at_end
- --push_to_hub
- --use_auth_token
- ${args}
method: random
metric:
  goal: maximize
  name: eval/bleu
parameters:
  model_name_or_path:
    value: ./
  task:
    value: covost2
  language:
    value: fr.en
  eval_split_name:
    value: test
  output_dir:
    value: ./
  num_train_epochs:
    value: 3
  per_device_train_batch_size:
    value: 8
  per_device_eval_batch_size:
    value: 8
  gradient_accumulation_steps:
    value: 8
  generation_max_length:
    value: 40
  generation_num_beams:
    value: 1
  learning_rate:
    distribution: log_uniform
    max: -6.9
    min: -9.2
  hidden_dropout:
    distribution: log_uniform
    max: -1.6
    min: -3.4
  warmup_steps:
    value: 500
  evaluation_strategy:
    value: steps
  max_duration_in_seconds:
    value: 20
  save_steps:
    value: 500
  eval_steps:
    value: 500
  logging_steps:
    value: 1
  metric_for_best_model:
    value: bleu
  greater_is_better:
    value: True
program: run_xtreme_s.py
project: xtreme_s_xlsr_2_bart_covost2_fr_en