up

sweep.yaml

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+command:
+- python3
+- ${program}
+- --overwrite_output_dir
+- --freeze_feature_encoder
+- --gradient_checkpointing
+- --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: ./output_dir
+  num_train_epochs:
+    value: 3
+  per_device_train_batch_size:
+    value: 4
+  per_device_eval_batch_size:
+    value: 4
+  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
+