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64 lines
3.3 KiB
Python
64 lines
3.3 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, FixKRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets.smolinstruct import NCExactMatchEvaluator, NCElementMatchEvaluator
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from opencompass.datasets import SmolInstructDataset
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from opencompass.utils.text_postprocessors import first_capital_postprocess
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nc_0shot_reader_cfg = dict(
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input_columns=['input'],
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output_column='output',
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train_split='validation')
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nc_hint_dict = {
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'I2F': """You are an expert chemist. Given the IUPAC representation of compounds, your task is to predict the molecular formula of the compound.
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The input contains the IUPAC representation of the compound. Your reply should contain only the molecular formula of the compound wrapped in <MOLFORMULA> and </MOLFORMULA> tags and no other text. Your reply must be valid and chemically reasonable.""",
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'I2S': """You are an expert chemist. Given the IUPAC representation of compounds, your task is to predict the SMILES representation of the compound.
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The input contains the IUPAC representation of the compound. Your reply should contain only the SMILES representation of the compound wrapped in <SMILES> and </SMILES> tags and no other text. Your reply must be valid and chemically reasonable.""",
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'S2F': """You are an expert chemist. Given the SMILES representation of compounds, your task is to predict the molecular formula of the compound.
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The input contains the SMILES representation of the compound. Your reply should contain only the molecular formula of the compound wrapped in <MOLFORMULA> and </MOLFORMULA> tags and no other text. Your reply must be valid and chemically reasonable.""",
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'S2I': """You are an expert chemist. Given the SMILES representation of compounds, your task is to predict the IUPAC representation of the compound.
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The input contains the SMILES representation of the compound. Your reply should contain only the IUPAC representation of the compound wrapped in <IUPAC> and </IUPAC> tags and no other text. Your reply must be valid and chemically reasonable.""",
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}
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name_dict = {
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'I2F': 'name_conversion-i2f',
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'I2S': 'name_conversion-i2s',
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'S2F': 'name_conversion-s2f',
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'S2I': 'name_conversion-s2i',
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}
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nc_0shot_instruct_datasets = []
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for _name in name_dict:
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_hint = nc_hint_dict[_name]
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nc_0shot_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=f'{_hint}\nQuestion: {{input}}\nAnswer: ',
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# template=f'<s>[INST] {{input}} [/INST]',
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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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if _name in ['I2F', 'S2F']:
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nc_0shot_eval_cfg = dict(
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evaluator=dict(type=NCElementMatchEvaluator),
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)
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else:
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nc_0shot_eval_cfg = dict(
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evaluator=dict(type=NCExactMatchEvaluator),
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)
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nc_0shot_instruct_datasets.append(
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dict(
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abbr=f'NC-{_name}-0shot-instruct',
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type=SmolInstructDataset,
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path='osunlp/SMolInstruct',
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name=name_dict[_name],
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reader_cfg=nc_0shot_reader_cfg,
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infer_cfg=nc_0shot_infer_cfg,
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eval_cfg=nc_0shot_eval_cfg,
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))
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del _name
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