config.yaml

defaults:
  # The available prompt selection methods are "MI, MI_G, MI_L, MI_GL, GE, GE_M, LE, MDL, MDL_M, ZLP, ZPM, ZMV, PPL, PPL_L"
  - method: MI
  - dataset: sst2
  - prompt: base_prompts
  - decoder: opt-2.7b
  - calibration: cbm-softmax
  - _self_


# If true, only get P(y|x,t) using a first token, not all tokens
first_token: false

# If true, get sum log prediction probability (not mean)
sum_log_prob: false

# fix the size of dataset.
num_samples: 1000

# fix random seed.
seed: 42

# If you want a fewshot evaluation, pass this argument a value corresponding to the number of shots you want.
# You can choose from '1', '2', '4', '1,2', '1,4', '2,4', and '1,2,4'."
fewshot: null

# Decide whether to evaluate when extracting p(y|x,t)
do_eval: true

# Whether or not to use mixed precision training. Choose from ‘no’, ‘fp16’, ‘bf16 or ‘fp8’.
mixed_precision: 'no'

# Choose whether to filter prompts in the way described in the ZPS paper (https://arxiv.org/abs/2211.04668).
# If true, the prompts will be filtered.
filter: false

# If true, the dataset is set to be unbalanced in the dynamic task.
unbalance: false