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46cc7894e1
* [Feature] Support import configs/models/summarizers from whl * Update LCBench configs * Update * Update * Update * Update * update * Update * Update * Update * Update * Update
44 lines
1.7 KiB
Python
44 lines
1.7 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 AccEvaluator
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from opencompass.utils.text_postprocessors import first_option_postprocess
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from opencompass.datasets import InfiniteBenchcoderunDataset
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InfiniteBench_coderun_reader_cfg = dict(
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input_columns=['context', 'func', 'func_call'],
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output_column='answer',
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)
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InfiniteBench_coderun_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='Following is a set of Python functions. There is a function called named {func}.\n\n{context}\n\nPlease give me the exact number of the return value of {func_call}. Be concise. Your response must end with the final returned value.'),
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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=5)
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)
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InfiniteBench_coderun_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'),
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pred_role='BOT'
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)
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InfiniteBench_coderun_datasets = [
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dict(
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type=InfiniteBenchcoderunDataset,
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abbr='InfiniteBench_coderun',
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path='./data/InfiniteBench/code_run.jsonl',
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reader_cfg=InfiniteBench_coderun_reader_cfg,
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infer_cfg=InfiniteBench_coderun_infer_cfg,
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eval_cfg=InfiniteBench_coderun_eval_cfg)
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]
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