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dfd9ac0fd9
* add judgellm prompts * add judgelm prompts * update import info * fix situation that no abbr in config * fix situation that no abbr in config * add summarizer for other judgellm * change config name * add maxlen * add maxlen * dict assert * dict assert * fix strings * fix strings
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
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.openicl.icl_inferencer import GenInferencer
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from opencompass.openicl.icl_evaluator import LMEvaluator
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from opencompass.datasets.subjective_cmp import SubjectiveCmpDataset
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subjective_reader_cfg = dict(
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input_columns=['question', 'index', 'reference_answer', 'evaluating_guidance', 'capability', 'prompt'],
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output_column='judge',
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train_split='test')
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subjective_all_sets = [
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"creation_v0.1",
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]
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subjective_datasets = []
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for _name in subjective_all_sets:
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subjective_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt="{question}"
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),
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]),
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=2048),
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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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cmp_order='both',
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin=[
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dict(
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role="SYSTEM",
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fallback_role="HUMAN",
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prompt="{prompt}"
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),
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],
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round=[dict(role="HUMAN",
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prompt="回答 1: <回答 1 开始> {prediction} <回答 1 结束>\n回答 2: <回答 2 开始> {prediction2} <回答 2 结束>\n")]))),
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pred_role="BOT",
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)
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subjective_datasets.append(
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dict(
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abbr=f"{_name}",
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type=SubjectiveCmpDataset,
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path="./data/subjective/",
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name=_name,
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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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))
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