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46 lines
1.9 KiB
Python
46 lines
1.9 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.openicl.icl_evaluator import EMEvaluator, RougeEvaluator, SquadEvaluator, AccEvaluator
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from opencompass.datasets.leval import LEvalTopicRetrievalDataset
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from opencompass.utils.text_postprocessors import first_capital_postprocess, first_capital_postprocess_multi, general_postprocess
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LEval_tr_reader_cfg = dict(
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input_columns=['context', 'question'],
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output_column='answer',
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train_split='test',
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test_split='test'
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)
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LEval_tr_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin=[
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dict(role='SYSTEM', fallback_role='HUMAN', prompt='Below is a record of our previous conversation on many different topics. You are the ASSISTANT, and I am the USER. At the beginning of each topic, the USER will say \'I would like to discuss the topic of <TOPIC>\'. Memorize each <TOPIC>. At the end of the record, I will ask you to retrieve the first/second/third topic names. Now the record start.'),
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],
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round=[
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dict(role='HUMAN', prompt='Document is as follows.\n{context}\nQuestion:{question}\nAnswer:'),
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dict(role='BOT', prompt=''),
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], )),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=30)
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)
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LEval_tr_eval_cfg = dict(
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evaluator=dict(type=EMEvaluator),
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pred_postprocessor=dict(type=general_postprocess),
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pred_role='BOT'
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)
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LEval_tr_datasets = [
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dict(
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type=LEvalTopicRetrievalDataset,
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abbr='LEval_topic_retrieval',
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path='L4NLP/LEval',
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name='topic_retrieval_longchat',
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reader_cfg=LEval_tr_reader_cfg,
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infer_cfg=LEval_tr_infer_cfg,
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eval_cfg=LEval_tr_eval_cfg)
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]
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