dataset_path: lmms-lab/VATEX
dataset_name: vatex_test
task: vatex_test
test_split: test
output_type: generate_until
doc_to_visual: !function utils.vatex_test_doc_to_visual
doc_to_text: !function utils.vatex_test_doc_to_text
doc_to_target: "answer"

process_results: !function utils.vatex_process_result
# 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: vatex_Bleu_4
    aggregation: !function utils.vatex_bleu4
    higher_is_better: true
  - metric: vatex_METEOR
    aggregation: !function utils.vatex_meteor
    higher_is_better: true
  - metric: vatex_ROUGE_L
    aggregation: !function utils.vatex_rougel
    higher_is_better: true
  - metric: vatex_CIDEr
    aggregation: !function utils.vatex_cider
    higher_is_better: true
metadata:
  version: 0.0

dataset_kwargs:
  token: True
  video: True #skip download video from hf
  # force_unzip: True
  cache_dir: vatex_test
  # From_YouTube: True

lmms_eval_specific_kwargs:
  default:
    prompt: Provide a brief single-sentence caption for the last video below. Do not give any reasoning, just the caption. You must follow the captioning style of the preceding videos. Do not start your response with "Output:", just provide the caption.
  gemini_api:
    prompt: Provide a brief single-sentence caption for the last video below. Do not give any reasoning, just the caption. You must follow the captioning style of the preceding videos. Do not start your response with "Output:", just provide the caption.

generation_kwargs:
  max_new_tokens: 64
  temperature: 0
  top_p: 1.0
  num_beams: 1
  do_sample: false
