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* [Feature] Support import configs/models/summarizers from whl * Update LCBench configs * Update * Update * Update * Update * update * Update * Update * Update * Update * Update
41 lines
1.4 KiB
Python
41 lines
1.4 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.datasets import InfiniteBenchenqaDataset, LongBenchF1Evaluator
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InfiniteBench_enqa_reader_cfg = dict(
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input_columns=['context', 'question'],
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output_column='answer',
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)
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InfiniteBench_enqa_infer_cfg = dict(
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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(role='SYSTEM', fallback_role='HUMAN', prompt='You are a helpful assistant.'),
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],
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round=[
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dict(role='HUMAN', prompt='Read the book below and answer a question.\n\n{context}\n\nQuestion: {question}\n\nBe very concise.'),
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dict(role='BOT', prompt=''),
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], )),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=40)
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)
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InfiniteBench_enqa_eval_cfg = dict(
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evaluator=dict(type=LongBenchF1Evaluator),
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pred_role='BOT'
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)
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InfiniteBench_enqa_datasets = [
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dict(
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type=InfiniteBenchenqaDataset,
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abbr='InfiniteBench_enqa',
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path='./data/InfiniteBench/longbook_qa_eng.jsonl',
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reader_cfg=InfiniteBench_enqa_reader_cfg,
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infer_cfg=InfiniteBench_enqa_infer_cfg,
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eval_cfg=InfiniteBench_enqa_eval_cfg)
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]
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