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add chinese version of humaneval, mbpp (#743)
* add chinese_version of humaneval,mbpp * add humaneval&mbpp gen.py * minor fix * minor add --------- Co-authored-by: yingfhu <yingfhu@gmail.com>
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Connor-Shen
GitHub
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4
configs/datasets/humaneval_cn/humaneval_cn_gen.py
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4
configs/datasets/humaneval_cn/humaneval_cn_gen.py
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from mmengine.config import read_base
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with read_base():
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from .humaneval_cn_gen_6313aa import humaneval_cn_datasets # noqa: F401, F403
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37
configs/datasets/humaneval_cn/humaneval_cn_gen_6313aa.py
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configs/datasets/humaneval_cn/humaneval_cn_gen_6313aa.py
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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 HumanevalDataset, HumanEvaluator, humaneval_postprocess_v2
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humaneval_reader_cfg = dict(
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input_columns=['prompt'], output_column='task_id', train_split='test')
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# TODO: allow empty output-column
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humaneval_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(
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role='HUMAN',
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prompt='完成以下Python代码任务:\n{prompt}'),
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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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humaneval_eval_cfg = dict(
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evaluator=dict(type=HumanEvaluator),
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pred_role='BOT',
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k=[1, 10, 100], # the parameter only for humaneval
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pred_postprocessor=dict(type=humaneval_postprocess_v2),
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)
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humaneval_cn_datasets = [
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dict(
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abbr='openai_humaneval_cn',
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type=HumanevalDataset,
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path='./data/humaneval_cn/human-eval-cn-v2-20210705.jsonl',
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reader_cfg=humaneval_reader_cfg,
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infer_cfg=humaneval_infer_cfg,
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eval_cfg=humaneval_eval_cfg)
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]
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1
configs/datasets/humaneval_cn/humaneval_cn_passk_gen_6313aa.py
Symbolic link
1
configs/datasets/humaneval_cn/humaneval_cn_passk_gen_6313aa.py
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./humaneval_cn_gen_6313aa.py
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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 HumanevalDataset, HumanEvaluator, humaneval_postprocess_v2
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humaneval_reader_cfg = dict(
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input_columns=['prompt'], output_column='task_id', train_split='test')
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# TODO: allow empty output-column
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humaneval_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(
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role='HUMAN',
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prompt='完成以下Python代码任务:\n{prompt}'),
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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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humaneval_eval_cfg = dict(
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evaluator=dict(type=HumanEvaluator),
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pred_role='BOT',
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k=[1, 10, 100], # the parameter only for humaneval
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pred_postprocessor=dict(type=humaneval_postprocess_v2),
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)
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humaneval_cn_datasets = [
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dict(
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abbr='openai_humaneval_cn_pass10',
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type=HumanevalDataset,
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path='./data/humaneval_cn/human-eval-cn-v2-20210705.jsonl',
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num_repeats=10,
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reader_cfg=humaneval_reader_cfg,
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infer_cfg=humaneval_infer_cfg,
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eval_cfg=humaneval_eval_cfg)
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]
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@ -4,7 +4,7 @@ from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import SanitizedMBPPDataset, MBPPEvaluator
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sanitized_mbpp_reader_cfg = dict(
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input_columns=['text', 'test_list'], output_column='test_list')
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input_columns=['text', 'test_list'], output_column='test_list_2')
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sanitized_mbpp_infer_cfg = dict(
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prompt_template=dict(
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4
configs/datasets/mbpp_cn/mbpp_cn_gen.py
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configs/datasets/mbpp_cn/mbpp_cn_gen.py
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from mmengine.config import read_base
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with read_base():
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from .mbpp_cn_gen_1d1481 import mbpp_cn_datasets # noqa: F401, F403
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64
configs/datasets/mbpp_cn/mbpp_cn_gen_1d1481.py
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64
configs/datasets/mbpp_cn/mbpp_cn_gen_1d1481.py
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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 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)\n assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4) \n assert 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 \n assert is_not_prime(10) == True \n assert 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] \n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \n assert 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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64
configs/datasets/mbpp_cn/mbpp_cn_passk_gen_1d1481.py
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64
configs/datasets/mbpp_cn/mbpp_cn_passk_gen_1d1481.py
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@ -0,0 +1,64 @@
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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 MBPPDataset_V2, MBPPPassKEvaluator
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mbpp_reader_cfg = dict(
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input_columns=['text', 'test_list'], output_column='test_column')
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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)\n assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4) \n assert 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 \n assert is_not_prime(10) == True \n assert 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] \n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \n assert 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=MBPPPassKEvaluator), pred_role="BOT")
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mbpp_cn_datasets = [
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dict(
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type=MBPPDataset_V2,
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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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65
configs/datasets/mbpp_cn/mbpp_cn_repeat10_gen_1d1481.py
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65
configs/datasets/mbpp_cn/mbpp_cn_repeat10_gen_1d1481.py
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@ -0,0 +1,65 @@
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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 MBPPDataset_V2, MBPPPassKEvaluator
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mbpp_reader_cfg = dict(
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input_columns=['text', 'test_list'], output_column='test_column')
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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)\n assert similar_elements((1, 2, 3, 4),(5, 4, 3, 7)) == (3, 4) \n assert 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 \n assert is_not_prime(10) == True \n assert 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] \n assert heap_queue_largest( [25, 35, 22, 85, 14, 65, 75, 22, 58],2)==[85, 75] \n assert 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=MBPPPassKEvaluator), pred_role="BOT")
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mbpp_cn_datasets = [
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dict(
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type=MBPPDataset_V2,
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abbr='mbpp_cn_pass10',
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path='./data/mbpp_cn/mbpp_cn.jsonl',
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num_repeats=10,
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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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@ -93,7 +93,10 @@ class SanitizedMBPPDataset(BaseDataset):
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def processing_test(example):
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example['text'] = example.pop('prompt')
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# used for prompt
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example['test_list'] = '\n'.join(example['test_list'])
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# used for eval
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example['test_list_2'] = example['test_list']
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example['test_column'] = dict(test_list_2=example['test_list'],
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task_id=example['task_id'])
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return example
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