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* [Dataset] Add SmolInstruct, Update Chembench * Add dataset metadata * update * update * update
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
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 MeteorEvaluator
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from opencompass.datasets import SmolInstructDataset
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meteor_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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meteor_hint_dict = {
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'MC': """You are an expert chemist. Given the SMILES representation of a molecule, your task is to describe the molecule in natural language.
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The input contains the SMILES representation of the molecule. Your reply should contain a natural language description of the molecule. Your reply must be valid and chemically reasonable.""",
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}
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name_dict = {
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'MC': 'molecule_captioning',
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}
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meteor_datasets = []
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for _name in meteor_hint_dict:
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_hint = meteor_hint_dict[_name]
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meteor_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt=f'{_hint}\nQuestion: {{input}}\nAnswer: '
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),
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dict(role='BOT', prompt='{output}\n')
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]),
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),
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prompt_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(
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role='HUMAN',
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prompt=f'{_hint}\nQuestion: {{input}}\nAnswer: '
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),
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],
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),
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ice_token='</E>',
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),
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retriever=dict(type=FixKRetriever, fix_id_list=[0]),
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inferencer=dict(type=GenInferencer),
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)
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meteor_eval_cfg = dict(
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evaluator=dict(type=MeteorEvaluator),
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)
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meteor_datasets.append(
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dict(
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abbr=f'{_name}',
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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=meteor_reader_cfg,
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infer_cfg=meteor_infer_cfg,
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eval_cfg=meteor_eval_cfg,
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))
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del _name, _hint
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