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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (DS1000Dataset, ds1000_postprocess,
ds1000_matplotlib_postprocess,
DS1000Evaluator)
ds1000_reader_cfg = dict(
input_columns=["prompt"],
output_column="test_column",
train_split='test',
test_split='test')
ds1000_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role="HUMAN",
prompt="{prompt}",
),
]),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer),
)
ds1000_eval_cfg = dict(
evaluator=dict(type=DS1000Evaluator),
pred_role="BOT",
pred_postprocessor=dict(type=ds1000_postprocess),
)
# The DS-1000 dataset can be downloaded from
# https://github.com/HKUNLP/DS-1000/blob/main/ds1000_data.zip
ds1000_datasets = [
dict(
abbr=f"ds1000_{lib}",
type=DS1000Dataset,
path="./data/ds1000_data/",
libs=f"{lib}",
reader_cfg=ds1000_reader_cfg,
infer_cfg=ds1000_infer_cfg,
eval_cfg=ds1000_eval_cfg,
) for lib in [
'Pandas',
'Numpy',
'Tensorflow',
'Scipy',
'Sklearn',
'Pytorch',
]
]
ds1000_datasets.append(
dict(
abbr="ds1000_Matplotlib",
type=DS1000Dataset,
path="./data/ds1000_data/",
libs="Matplotlib",
reader_cfg=ds1000_reader_cfg,
infer_cfg=ds1000_infer_cfg,
eval_cfg=dict(
evaluator=dict(type=DS1000Evaluator),
pred_role="BOT",
pred_postprocessor=dict(type=ds1000_matplotlib_postprocess),
),
))
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