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68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
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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.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import (DS1000Dataset, ds1000_postprocess,
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ds1000_matplotlib_postprocess,
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DS1000Evaluator)
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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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evaluator=dict(type=DS1000Evaluator),
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pred_role="BOT",
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pred_postprocessor=dict(type=ds1000_postprocess),
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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="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,
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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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]
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]
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ds1000_datasets.append(
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dict(
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abbr="ds1000_Matplotlib",
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type=DS1000Dataset,
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path="ds1000_data/",
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libs="Matplotlib",
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reader_cfg=ds1000_reader_cfg,
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infer_cfg=ds1000_infer_cfg,
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eval_cfg=dict(
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evaluator=dict(type=DS1000Evaluator),
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pred_role="BOT",
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pred_postprocessor=dict(type=ds1000_matplotlib_postprocess),
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),
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))
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