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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: grid
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: 16
generation_max_length:
value: 40
generation_num_beams:
value: 1
learning_rate:
values:
- 3e-5
- 3e-4
hidden_dropout:
value: 0.2
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
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