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004ed79593
* add TheoremQA with 5-shot * cherry pick from add-huggingface-above-v4.33, good TheoremQA results
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
from mmengine.config import read_base
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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 TheoremQADatasetV3, TheoremQA_postprocess_v3, TheoremQAEvaluatorV3
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with read_base():
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from .TheoremQA_few_shot_examples import examples
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num_shot = 5
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rounds = []
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for index, (query, response) in enumerate(examples[:num_shot]):
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if index == 0:
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desc = "You are supposed to provide a solution to a given problem.\n\n"
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else:
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desc = ""
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rounds += [
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dict(role="HUMAN", prompt=f"{desc}Problem:\n{query}\nSolution:"),
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dict(role="BOT", prompt=f"{response}")
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]
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rounds += [dict(role="HUMAN", prompt="Problem:\n{Question}\nSolution:")]
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TheoremQA_reader_cfg = dict(input_columns=["Question", "Answer_type"], output_column="Answer", train_split="test", test_split="test")
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TheoremQA_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template=dict(round=rounds)),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=1024, stopping_criteria=["Problem:", "Problem"]),
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)
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TheoremQA_eval_cfg = dict(
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evaluator=dict(type=TheoremQAEvaluatorV3),
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pred_postprocessor=dict(type=TheoremQA_postprocess_v3)
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)
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TheoremQA_datasets = [
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dict(
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abbr="TheoremQA",
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type=TheoremQADatasetV3,
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path="data/TheoremQA/theoremqa_test.json",
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reader_cfg=TheoremQA_reader_cfg,
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infer_cfg=TheoremQA_infer_cfg,
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eval_cfg=TheoremQA_eval_cfg,
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)
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]
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