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