prompt_agent_cn.yml

task_name: thunews # bigbench | ncbi | ... | or your own task
search_algo_name: beam_search # mcts | beam_search
world_model_name: beam_search
language: cn
print_log: true
instruction: 请根据以下新闻内容判断其所属的主要类别。如果新闻涉及多个方面，请选择最核心的主题。类别包括但不限于：财经（与经济、金融相关）、彩票（与彩票、博彩相关）、房产（与房地产市场、房屋买卖租赁直接相关）、股票（与股市、证券相关）、家居（与家庭生活、装修装饰相关）、教育（与学校、学习、培训相关）、科技（与科学技术、创新发明相关）、社会（与公共安全、法律纠纷、社会现象等广泛的社会事件相关）、时尚（与时装、美容、潮流相关）、时政（与政治、政府政策相关）、体育（与运动、竞技相关）、星座（与占星术、运势预测相关）、游戏（与电子游戏、桌游相关）、娱乐（与电影、音乐、明星八卦相关）。
train_size: 200
eval_size: 0 # data split for reward calculation
test_size: 0 # if test_size is not 0, the optimized nodes will be tested at last.
seed: 0 # if need to fixed shuffled dataset
data_dir: Path/to/PromptScope/prompt_scope/datasets/benchmarks/thunews/sample_train_data.jsonl # if data is downloaded
# Note: the current supported bigbench tasks are specified by
# data_dir using the same task_name (bigbench), if there is not
# specific .py class inplemented in the tasks folder.
post_instruction: false # false: prompt + task question | true: task question + prompt
eval_type: custom

iteration_num: 10
expand_width: 3 # num of branches of each node
depth_limit: 5 # the max depth of mcts
# mcts setting
min_depth: 2 # min depth of mcts for early stop
w_exp: 2.5 # balance exploration and exploitation
# beam search setting
beam_width: 3

# mcts world model setting
train_shuffle: true
num_new_prompts: 1 # 3 if beam search
train_batch_size: 5
prompt_length_limit: 200