File size: 1,575 Bytes
56c27a3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import TabMWPDataset, TabMWPEvaluator
# None of the TabMWP dataset in huggingface is correctly parsed, so we use our own dataset reader
# Please download the dataset from https://github.com/lupantech/PromptPG/tree/main
input_format='TQ'
output_format='A'
elements = {'Q': 'Question: {question}',
'T': 'Table: {table}',
'S': 'Solution: {solution}',
'A': 'Answer: The answer is {answer}.',
'AS': 'Answer: The answer is {answer}. BECAUSE: {solution}',
'SA': 'Answer: {solution} The answer is {answer}.'}
TabMWP_reader_cfg = dict(
input_columns=['question', 'table'],
output_column='test_elements',
train_split='dev',
)
TabMWP_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(
role='HUMAN',
prompt= '\n'.join(elements[label] for label in input_format)
),
],
),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer),
)
TabMWP_eval_cfg = dict(
evaluator=dict(type=TabMWPEvaluator)
)
TabMWP_datasets = [
dict(
type=TabMWPDataset,
path='./data/tabmwp/',
reader_cfg=TabMWP_reader_cfg,
infer_cfg=TabMWP_infer_cfg,
eval_cfg=TabMWP_eval_cfg,)
]
|