wandb sweep invalid float value

Question

I am doing hyperparamater optimization with wandb sweep. I defined epoch, learning_rate and other values in the parameters, and then past them to the wandb command. However, when I run sweep, it always shows error "run_ner.py: error: argument --learning_rate: invalid float value: '${learning_rate}'", or "${epoch}, ${batch_size}". It seems every variable I referred to using ${VAR} wasn't processed correctly.

Here is an example of my sweep.yaml

program: run_ner.py
method: random
metric:
  name: f1
  goal: maximize
parameters:
  batch_size:
    values: [16, 32, 64, 128]
  model_name_or_path:
    values: ["a_bert_model","b_bert_model"]
  weight_decay:
    distribution: "uniform"
    min: 0.0
    max: 0.1
  learning_rate:
    distribution: "uniform"
    min: 0.001
    max: 0.01
  epochs:
    values: [400]
  datadir:
    values: ["fullData","partialData"]
  max_seq_length:
    values: [128, 256, 512]
  do_lower_case:
    values: [True, False]
  pad_to_max_length:
    values: [True, False]
command:
  - ${env}
  - python
  - run_ner.py
  - --model_name_or_path=${model_name_or_path}
  - --train_file="temp"
  - --do_train
  - --validation_file="temp"
  - --test_file="temp"
  - --do_predict
  - --do_eval
  - --num_train_epochs=${epochs}
  - --text_column_name="words"
  - --label_column_name="labels"
  - --seed=42
  - --per_device_train_batch_size=${batch_size}
  - --per_device_eval_batch_size=${batch_size}
  - --learning_rate=${learning_rate}
  - --weight_decay=${weight_decay}
  - --label_all_tokens=True
  - --load_best_model_at_end=True
  - --save_strategy=steps
  - --evaluation_strategy=epoch
  - --logging_strategy=epoch
  - --max_seq_length=${max_seq_length}
  - --pad_to_max_length=${pad_to_max_length}
  - --output_dir="outputs/"

And a snippet of the execution output.

run_ner.py: error: argument --learning_rate: invalid float value: '${learning_rate}'
2024-03-04 13:22:56,058 - wandb.wandb_agent - INFO - Agent received command: run
2024-03-04 13:22:56,058 - wandb.wandb_agent - INFO - Agent starting run with config:
    datadir: fullData
    do_lower_case: True
    learning_rate: 0.0067381448491156066
    max_seq_length: 512
    model_name_or_path: a_bert_model
    pad_to_max_length: False
    weight_decay: 0.003210073163495664
2024-03-04 13:22:56,071 - wandb.wandb_agent - INFO - About to run command: /usr/bin/env python run_ner.py --model_name_or_path=${model_name_or_path} --train_file="temp" --do_train --validation_file="temp" --test_file="temp" --do_predict --do_eval --num_train_epochs=400 --text_column_name="words" --label_column_name="labels" --seed=42 --per_device_train_batch_size=32 --per_device_eval_batch_size=32 --learning_rate=${learning_rate} --weight_decay=${weight_decay} --label_all_tokens=True --load_best_model_at_end=True --save_strategy=steps --evaluation_strategy=epoch --logging_strategy=epoch --max_seq_length=${max_seq_length} --pad_to_max_length=${pad_to_max_length} --output_dir="outputs/"