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75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
from opencompass.datasets import WildBenchDataset, wildbench_bradleyterry_postprocess
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from opencompass.openicl.icl_evaluator import LMEvaluator
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from opencompass.openicl.icl_inferencer import ChatInferencer, GenInferencer
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from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.summarizers import WildBenchPairSummarizer
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subjective_reader_cfg = dict(
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input_columns=['dialogue', 'prompt'],
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output_column='judge',
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)
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data_path = './data/subjective/WildBench/wildbench.jsonl'
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wildbench_datasets = []
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subjective_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template="""{dialogue}"""),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=ChatInferencer, max_seq_len=32768, infer_mode='last'),
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)
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subjective_eval_cfg = dict(
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evaluator=dict(
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type=LMEvaluator,
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prompt_template=dict(type=PromptTemplate, template="""{prompt}"""),
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dict_postprocessor=dict(type=wildbench_bradleyterry_postprocess),
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keep_predictions=True, # Must be turned on to save predictions from model pairs to calculate style features in postprocessor
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),
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pred_role='BOT',
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)
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base_models = [
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dict(
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abbr='gpt4-turbo',
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),
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dict(
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abbr='HaiKu',
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),
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dict(
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abbr='llama-2-70b-chat-hf',
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),
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]
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wildbench_datasets.append(
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dict(
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abbr='wildbench',
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type=WildBenchDataset,
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path=data_path,
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eval_mode='pair',
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reader_cfg=subjective_reader_cfg,
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infer_cfg=subjective_infer_cfg,
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eval_cfg=subjective_eval_cfg,
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given_pred=[
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{'abbr': 'gpt4-turbo', 'path': './data/subjective/WildBench/gpt4'},
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{
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'abbr': 'llama-2-70b-chat-hf',
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'path': './data/subjective/WildBench/llama2-70b',
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},
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{'abbr': 'HaiKu', 'path': './data/subjective/WildBench/claude'},
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{
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'abbr': 'llama-2-70b-chat-turbomind',
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'path': './data/subjective/WildBench/llama2-70b',
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},
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{
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'abbr': 'llama-2-70b-chat-vllm',
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'path': './data/subjective/WildBench/llama2-70b',
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},
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],
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mode='m2n', # m个模型 与 n个模型进行对战
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infer_order='random',
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base_models=base_models,
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)
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)
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