dataset_path: yifanzhang114/MME-RealWorld-Lmms-eval
dataset_kwargs:
  token: True
  # From_YouTube: True
task: mmerealworld
test_split: train
output_type: generate_until
doc_to_visual: !function utils.mme_realworld_doc_to_visual
doc_to_text: !function utils.mme_realworld_doc_to_text
doc_to_target: "answer"
generation_kwargs:
  max_new_tokens: 16
  temperature: 0
  top_p: 1.0
  num_beams: 1
  do_sample: false
# The return value of process_results will be used by metrics
process_results: !function utils.mme_realworld_process_results
# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
metric_list:
  - metric: mme_realworld_score
    aggregation: !function utils.mme_realworld_aggregate_results
    higher_is_better: true
lmms_eval_specific_kwargs:
  default:
    pre_prompt: ""
    post_prompt: "\nSelect the best answer to the above multiple-choice question based on the image. Respond with only the letter (A, B, C, D, or E) of the correct option."
  gpt4v:
    pre_prompt: ""
    post_prompt: "\nSelect the best answer to the above multiple-choice question based on the image. Respond with only the letter (A, B, C, D, or E) of the correct option."
  xcomposer2_4khd:
    pre_prompt: "[UNUSED_TOKEN_146]user\n"
    post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
metadata:
  - version: 0.0
