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* [Feature] Support import configs/models/summarizers from whl * Update LCBench configs * Update * Update * Update * Update * update * Update * Update * Update * Update * Update
58 lines
2.5 KiB
Python
58 lines
2.5 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import BM25Retriever
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from opencompass.openicl.icl_inferencer import AttackInferencer
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from opencompass.openicl.icl_evaluator import BleuEvaluator
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from opencompass.datasets import IWSLT2017Dataset
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from opencompass.utils.text_postprocessors import general_cn_postprocess
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iwslt2017_reader_cfg = dict(
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input_columns='en', output_column='de', train_split='validation')
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original_prompt_list = [
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'Translate the provided sentence from English to German while maintaining the original meaning and context:',
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'Convert the following sentence from its original English language to the target language German:',
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'Given the sentence below, perform a machine translation from English to German:',
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'Translate the subsequent sentence from its source language English into the desired language German:',
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'Accurately translate the sentence from English to German, ensuring the meaning remains intact:',
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'Please perform a translation of the given sentence, converting it from English to German:',
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'Translate the following text from the source language English to the target language German:',
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'Using machine translation, convert the given sentence from English into the German language:',
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'Translate the subsequent text passage from its original English language to the German language:',
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'Perform a machine translation for the provided sentence, changing it from English to German:',
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]
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iwslt2017_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template=dict(
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begin='</E>',
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round=[
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dict(role='HUMAN', prompt='{adv_prompt}\n{en}'),
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dict(role='BOT', prompt='{de}'),
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]
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),
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ice_token='</E>'),
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retriever=dict(type=BM25Retriever, ice_num=1),
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inferencer=dict(
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type=AttackInferencer,
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original_prompt_list=original_prompt_list,
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adv_key='adv_prompt',
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metric_key='score'))
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iwslt2017_eval_cfg = dict(
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evaluator=dict(type=BleuEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=general_cn_postprocess),
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dataset_postprocessor=dict(type=general_cn_postprocess))
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iwslt2017_datasets = [
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dict(
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abbr='iwslt',
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type=IWSLT2017Dataset,
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path='iwslt2017',
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name='iwslt2017-en-de',
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reader_cfg=iwslt2017_reader_cfg,
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infer_cfg=iwslt2017_infer_cfg,
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eval_cfg=iwslt2017_eval_cfg)
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]
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