dataset_path: initiacms/XLRS-Bench-lite
task: xlrs-lite
test_split: train
output_type: generate_until
doc_to_visual: !function mcq_utils.xlrs_doc_to_visual
doc_to_text: !function mcq_utils.xlrs_doc_to_text
doc_to_target: "answer"
generation_kwargs:
  max_new_tokens: 64
  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 mcq_utils.xlrs_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: xlrs_micro_score
    aggregation: !function mcq_utils.xlrs_aggregate_results
    higher_is_better: true
  - metric: xlrs_macro_score
    aggregation: !function mcq_utils.xlrs_aggregate_results_macro_score
    higher_is_better: true
lmms_eval_specific_kwargs:
  default:
    pre_prompt: ""
    post_prompt: ""
metadata:
  - version: 0.0
