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44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
# USED IN BASE MODEL
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from mmengine.config import read_base
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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 DropOpenAIDataset, DropOpenAIEvaluator
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with read_base():
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from .drop_examples import drop_examples # noqa: F401, F403
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drop_reader_cfg = dict(
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input_columns=['prompt'],
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output_column='answers',
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train_split='validation',
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test_split='validation',
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)
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template = f'''\
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You will be asked to read a passage and answer a question. Think step by step, then write a line of the form "Answer: $ANSWER" at the end of your response. Some examples of passages and Q&A are provided below.
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{drop_examples}
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# Your Task
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---
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{{prompt}}'''
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drop_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template=dict(round=[dict(role='HUMAN', prompt=template)])),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, stopping_criteria=['---', 'Passage', 'Question', 'You will be asked']),)
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drop_eval_cfg = dict(evaluator=dict(type=DropOpenAIEvaluator))
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drop_datasets = [
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dict(
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abbr='drop',
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type=DropOpenAIDataset,
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path='data/drop_simple_eval/dev.jsonl',
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reader_cfg=drop_reader_cfg,
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infer_cfg=drop_infer_cfg,
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eval_cfg=drop_eval_cfg)
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]
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