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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
68 lines
1.7 KiB
Python
68 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.datasets import DS1000Dataset, DS1000ServiceEvaluator
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ds1000_reader_cfg = dict(
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input_columns=['prompt'],
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output_column='test_column',
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train_split='test',
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test_split='test')
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ds1000_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='{prompt}',
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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),
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)
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ds1000_eval_cfg_dict = {
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lib: dict(
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evaluator=dict(
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type=DS1000ServiceEvaluator,
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lib=lib,
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ip_address=
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'localhost', # replace to your code_eval_server ip_address, port
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port=5000
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),
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pred_role='BOT')
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for lib in [
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'Pandas',
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'Numpy',
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'Tensorflow',
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'Scipy',
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'Sklearn',
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'Pytorch',
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'Matplotlib',
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]
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}
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# The DS-1000 dataset can be downloaded from
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# https://github.com/HKUNLP/DS-1000/blob/main/ds1000_data.zip
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ds1000_datasets = [
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dict(
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abbr=f'ds1000_{lib}',
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type=DS1000Dataset,
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path='./data/ds1000_data/',
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libs=f'{lib}',
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reader_cfg=ds1000_reader_cfg,
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infer_cfg=ds1000_infer_cfg,
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eval_cfg=ds1000_eval_cfg_dict[lib],
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) for lib in [
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'Pandas',
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'Numpy',
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'Tensorflow',
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'Scipy',
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'Sklearn',
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'Pytorch',
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'Matplotlib',
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]
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]
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