mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
b2da1c08a8
* [Dataset] Add SmolInstruct, Update Chembench * Add dataset metadata * update * update * update
94 lines
4.1 KiB
Python
94 lines
4.1 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
|
|
from opencompass.openicl.icl_retriever import ZeroRetriever, FixKRetriever
|
|
from opencompass.openicl.icl_inferencer import GenInferencer
|
|
from opencompass.datasets.smolinstruct import NCExactMatchEvaluator, NCElementMatchEvaluator
|
|
from opencompass.datasets import SmolInstructDataset
|
|
from opencompass.utils.text_postprocessors import first_capital_postprocess
|
|
|
|
nc_reader_cfg = dict(
|
|
input_columns=['input'],
|
|
output_column='output',
|
|
train_split='validation')
|
|
|
|
nc_hint_dict = {
|
|
'I2F': """You are an expert chemist. Given the IUPAC representation of compounds, your task is to predict the molecular formula of the compound.
|
|
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.""",
|
|
'I2S': """You are an expert chemist. Given the IUPAC representation of compounds, your task is to predict the SMILES representation of the compound.
|
|
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.""",
|
|
'S2F': """You are an expert chemist. Given the SMILES representation of compounds, your task is to predict the molecular formula of the compound.
|
|
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.""",
|
|
'S2I': """You are an expert chemist. Given the SMILES representation of compounds, your task is to predict the IUPAC representation of the compound.
|
|
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.""",
|
|
}
|
|
|
|
name_dict = {
|
|
'I2F': 'name_conversion-i2f',
|
|
'I2S': 'name_conversion-i2s',
|
|
'S2F': 'name_conversion-s2f',
|
|
'S2I': 'name_conversion-s2i',
|
|
}
|
|
|
|
nc_datasets = []
|
|
for _name in nc_hint_dict:
|
|
_hint = nc_hint_dict[_name]
|
|
nc_infer_cfg = dict(
|
|
ice_template=dict(
|
|
type=PromptTemplate,
|
|
template=dict(round=[
|
|
dict(
|
|
role='HUMAN',
|
|
prompt=f'{_hint}\nQuestion: {{input}}\nAnswer: '
|
|
),
|
|
dict(role='BOT', prompt='{output}\n')
|
|
]),
|
|
),
|
|
prompt_template=dict(
|
|
type=PromptTemplate,
|
|
template=dict(
|
|
begin='</E>',
|
|
round=[
|
|
dict(
|
|
role='HUMAN',
|
|
prompt=f'{_hint}\nQuestion: {{input}}\nAnswer: '
|
|
),
|
|
],
|
|
),
|
|
ice_token='</E>',
|
|
),
|
|
retriever=dict(type=FixKRetriever, fix_id_list=[0]),
|
|
inferencer=dict(type=GenInferencer),
|
|
)
|
|
# nc_infer_cfg = dict(
|
|
# prompt_template=dict(
|
|
# type=PromptTemplate,
|
|
# template=dict(
|
|
# round=[
|
|
# dict(role='HUMAN', prompt=f'{_hint}\nQuestion: {{input}}\nAnswer: '),
|
|
# ],
|
|
# ),
|
|
# ),
|
|
# retriever=dict(type=ZeroRetriever),
|
|
# inferencer=dict(type=GenInferencer),
|
|
# )
|
|
if _name in ['I2F', 'S2F']:
|
|
nc_eval_cfg = dict(
|
|
evaluator=dict(type=NCElementMatchEvaluator),
|
|
)
|
|
else:
|
|
nc_eval_cfg = dict(
|
|
evaluator=dict(type=NCExactMatchEvaluator),
|
|
)
|
|
|
|
nc_datasets.append(
|
|
dict(
|
|
abbr=f'NC-{_name}',
|
|
type=SmolInstructDataset,
|
|
path='osunlp/SMolInstruct',
|
|
name=name_dict[_name],
|
|
reader_cfg=nc_reader_cfg,
|
|
infer_cfg=nc_infer_cfg,
|
|
eval_cfg=nc_eval_cfg,
|
|
))
|
|
|
|
del _name, _hint
|