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65 lines
3.5 KiB
Python
65 lines
3.5 KiB
Python
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 MBPPDataset, MBPPEvaluator
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mbpp_reader_cfg = dict(
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input_columns=['text', 'test_list'], output_column='test_list_2')
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mbpp_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=
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"你是一名专业的 Python 程序员，你的任务是：编写一个函数，从给定的两个元组列表中查找相似的元素。 你的代码应该通过这些测试：\n\n assert similar_elements((3, 4, 5, 6),(5, 7, 4, 10)) == (4, 5)\nassert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4) \nassert similar_elements((11, 12, 14, 13),(17, 15, 14, 13)) == (13, 14) \n"
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),
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dict(
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role="BOT",
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prompt=
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"[BEGIN]\n 'def similar_elements(test_tup1, test_tup2):\r\n res = tuple(set(test_tup1) & set(test_tup2))\r\n return (res)' \n[DONE] \n\n "
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),
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dict(
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role="HUMAN",
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prompt=
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"你是一名专业的 Python 程序员，你的任务是：编写一个 Python 函数来识别一个整数是否不是素数。 你的代码应该通过这些测试：\n\n assert is_not_prime(2) == False \nassert is_not_prime(10) == True \nassert is_not_prime(35) == True \n"
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),
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dict(
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role="BOT",
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prompt=
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"[BEGIN]\n 'import math\r\ndef is_not_prime(n):\r\n result = False\r\n for i in range(2,int(math.sqrt(n)) + 1):\r\n if n % i == 0:\r\n result = True\r\n return result' \n[DONE] \n\n "
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),
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dict(
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role="HUMAN",
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prompt=
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"你是一名专业的 Python 程序员，你的任务是：编写一个函数，使用堆队列算法从给定的数字列表中查找最大整数。 你的代码应该通过这些测试：\n\n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],3)==[85, 75, 65] \nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \nassert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],5)==[85, 75, 65, 58, 35] \n"
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),
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dict(
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role="BOT",
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prompt=
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"[BEGIN]\n 'import heapq as hq\r\ndef heap_queue_largest(nums,n):\r\n largest_nums = hq.nlargest(n, nums)\r\n return largest_nums' \n[DONE] \n\n "
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),
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dict(
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role="HUMAN",
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prompt=
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"你是一名专业的 Python 程序员，你的任务是: {text} 你的代码应该通过这些测试:\n\n {test_list} \n"
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),
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dict(role="BOT", prompt="[BEGIN]\n"),
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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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mbpp_eval_cfg = dict(evaluator=dict(type=MBPPEvaluator), pred_role="BOT")
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mbpp_cn_datasets = [
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dict(
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type=MBPPDataset,
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abbr='mbpp_cn',
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path='./data/mbpp_cn/mbpp_cn.jsonl',
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reader_cfg=mbpp_reader_cfg,
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infer_cfg=mbpp_infer_cfg,
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eval_cfg=mbpp_eval_cfg)
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]
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