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72 lines
3.1 KiB
Python
72 lines
3.1 KiB
Python
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import os
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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.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import ScibenchDataset, scibench_postprocess
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scibench_reader_cfg = dict(input_columns=['question'], output_column='answer')
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scibench_subsets = [
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"atkins",
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"calculus",
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"chemmc",
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"class",
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"diff",
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"fund",
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"matter",
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"quan",
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"stat",
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"thermo"
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]
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scibench_datasets = []
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for prompt_type in ["zs", "zs-cot", "fs", "fs-cot"]:
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for _name in scibench_subsets:
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if prompt_type == "fs":
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prompt_path = os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}_prompt.txt')
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elif prompt_type == "fs-cot":
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prompt_path = os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}_sol.txt')
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else:
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prompt_path = None
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if prompt_path is not None:
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with open(prompt_path, 'r') as f:
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_hint = f.read()
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else:
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_hint = ""
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human_prompt = {
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'zs': "Please provide a clear and step-by-step solution for a scientific problem in the categories of Chemistry, Physics, or Mathematics. The problem will specify the unit of measurement, which should not be included in the answer. Express the final answer as a decimal number with three digits after the decimal point. Conclude the answer by stating 'Therefore, the answer is \\boxed[ANSWER].'\n\nProblem: {question}\nAnswer:",
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'zs-cot': "Please provide a clear and step-by-step solution for a scientific problem in the categories of Chemistry, Physics, or Mathematics. The problem will specify the unit of measurement, which should not be included in the answer. Express the final answer as a decimal number with three digits after the decimal point. Conclude the answer by stating 'Therefore, the answer is \\boxed[ANSWER].'\n\nProblem: {question}\nAnswer:Let’s think step by step.",
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'fs': f"{_hint}\n\nProblem 6: {{question}}\nAnswer: ",
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'fs-cot': f"{_hint}\n\nProblem 6: {{question}}\nExplanation for Problem 6: ",
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}[prompt_type]
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scibench_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(role="HUMAN", prompt=human_prompt)
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])
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=512)
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)
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scibench_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type=scibench_postprocess))
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scibench_datasets.append(
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dict(
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type=ScibenchDataset,
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path="./data/scibench",
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name=_name,
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abbr= f"scibench-{_name}" if prompt_type == 'zs' else f"scibench-{_name}_{prompt_type}",
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reader_cfg=scibench_reader_cfg,
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infer_cfg=scibench_infer_cfg.copy(),
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eval_cfg=scibench_eval_cfg.copy()
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)
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)
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