dataset_path: NCSOFT/K-MMStar
dataset_kwargs:
  token: True
task: "mmstar_ko"
test_split: val
output_type: generate_until
doc_to_visual: !function ko_utils.mmstar_doc_to_visual
doc_to_text: !function ko_utils.mmstar_doc_to_text
doc_to_target: "answer"
# The return value of process_results will be used by metrics
process_results: !function ko_utils.mmstar_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: coarse perception
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
  - metric: fine-grained perception
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
  - metric: instance reasoning
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
  - metric: logical reasoning
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
  - metric: science & technology
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
  - metric: math
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
  - metric: average
    aggregation: !function ko_utils.mmstar_aggregate_results
    higher_is_better: true
lmms_eval_specific_kwargs:
  default:
    pre_prompt: ""
    post_prompt: ""
metadata:
  - version: 0.0
