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54 lines
1.5 KiB
Python
54 lines
1.5 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
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import TabMWPDataset, TabMWPEvaluator
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# None of the TabMWP dataset in huggingface is correctly parsed, so we use our own dataset reader
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# Please download the dataset from https://github.com/lupantech/PromptPG/tree/main
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input_format='TQ'
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output_format='A'
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elements = {"Q": "Question: {question}",
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"T": "Table: {table}",
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"S": "Solution: {solution}",
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"A": "Answer: The answer is {answer}.",
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"AS": "Answer: The answer is {answer}. BECAUSE: {solution}",
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"SA": "Answer: {solution} The answer is {answer}."}
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TabMWP_reader_cfg = dict(
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input_columns=["question", "table"],
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output_column="test_elements",
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train_split='dev',
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)
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TabMWP_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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round=[
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dict(
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role="HUMAN",
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prompt= "\n".join(elements[label] for label in input_format)
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),
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],
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),
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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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TabMWP_eval_cfg = dict(
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evaluator=dict(type=TabMWPEvaluator)
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)
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TabMWP_datasets = [
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dict(
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type=TabMWPDataset,
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path="./data/tabmwp/",
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reader_cfg=TabMWP_reader_cfg,
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infer_cfg=TabMWP_infer_cfg,
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eval_cfg=TabMWP_eval_cfg,)
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]
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