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[Feature] Support sanitized MBPP dataset (#745)

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configs/datasets/humaneval/humaneval_passk_gen_8e312c.py Symbolic link
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./humaneval_gen_8e312c.py

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configs/datasets/humaneval/humaneval_repeat10_gen_8e312c.py Normal file
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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import HumanevalDataset, HumanEvaluator, humaneval_postprocess
humaneval_reader_cfg = dict(
input_columns=['prompt'], output_column='task_id', train_split='test')
# TODO: allow empty output-column
humaneval_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt='Complete the following python code:\n{prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
humaneval_eval_cfg = dict(
evaluator=dict(type=HumanEvaluator),
pred_role='BOT',
k=[1, 10, 100], # the parameter only for humaneval
pred_postprocessor=dict(type=humaneval_postprocess),
)
humaneval_datasets = [
dict(
abbr='openai_humaneval_pass10',
type=HumanevalDataset,
path='./data/humaneval/human-eval-v2-20210705.jsonl',
num_repeats=10,
reader_cfg=humaneval_reader_cfg,
infer_cfg=humaneval_infer_cfg,
eval_cfg=humaneval_eval_cfg)
]

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configs/datasets/mbpp/mbpp_passk_gen_1e1056.py Normal file
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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator
mbpp_reader_cfg = dict(
input_columns=['text', 'test_list'], output_column='test_column')
mbpp_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\n assert is_not_prime(2) == False \n assert is_not_prime(10) == True \n assert is_not_prime(35) == True \n"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: {text} Your code should pass these tests:\n\n {test_list} \n"
),
dict(role="BOT", prompt="[BEGIN]\n"),
], )),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
mbpp_eval_cfg = dict(evaluator=dict(type=MBPPPassKEvaluator), pred_role="BOT")
mbpp_datasets = [
dict(
type=MBPPDataset_V2,
abbr='mbpp',
path='./data/mbpp/mbpp.jsonl',
reader_cfg=mbpp_reader_cfg,
infer_cfg=mbpp_infer_cfg,
eval_cfg=mbpp_eval_cfg)
]

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configs/datasets/mbpp/mbpp_repeat10_gen_1e1056.py Normal file
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# This config is used for pass@k evaluation with dataset repetition
# That model cannot generate multiple response for single input
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator
mbpp_reader_cfg = dict(
input_columns=['text', 'test_list'], output_column='test_column')
mbpp_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\n assert is_not_prime(2) == False \n assert is_not_prime(10) == True \n assert is_not_prime(35) == True \n"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: {text} Your code should pass these tests:\n\n {test_list} \n"
),
dict(role="BOT", prompt="[BEGIN]\n"),
], )),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
mbpp_eval_cfg = dict(evaluator=dict(type=MBPPPassKEvaluator), pred_role="BOT")
mbpp_datasets = [
dict(
type=MBPPDataset_V2,
abbr='mbpp_pass10',
path='./data/mbpp/mbpp.jsonl',
num_repeats=10,
reader_cfg=mbpp_reader_cfg,
infer_cfg=mbpp_infer_cfg,
eval_cfg=mbpp_eval_cfg)
]

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configs/datasets/mbpp/sanitized_mbpp_gen_1e1056.py Normal file
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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import SanitizedMBPPDataset, MBPPEvaluator
sanitized_mbpp_reader_cfg = dict(
input_columns=['text', 'test_list'], output_column='test_list')
sanitized_mbpp_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\n assert is_not_prime(2) == False \n assert is_not_prime(10) == True \n assert is_not_prime(35) == True \n"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: {text} Your code should pass these tests:\n\n {test_list} \n"
),
dict(role="BOT", prompt="[BEGIN]\n"),
], )),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
sanitized_mbpp_eval_cfg = dict(evaluator=dict(type=MBPPEvaluator), pred_role="BOT")
sanitized_mbpp_datasets = [
dict(
type=SanitizedMBPPDataset,
abbr='sanitized_mbpp',
path='./sanitized-mbpp.jsonl',
reader_cfg=sanitized_mbpp_reader_cfg,
infer_cfg=sanitized_mbpp_infer_cfg,
eval_cfg=sanitized_mbpp_eval_cfg)
]

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configs/datasets/mbpp/sanitized_mbpp_passk_gen_1e1056.py Normal file
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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import SanitizedMBPPDataset, MBPPPassKEvaluator
sanitized_mbpp_reader_cfg = dict(
input_columns=['text', 'test_list'], output_column='test_column')
sanitized_mbpp_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\n assert is_not_prime(2) == False \n assert is_not_prime(10) == True \n assert is_not_prime(35) == True \n"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: {text} Your code should pass these tests:\n\n {test_list} \n"
),
dict(role="BOT", prompt="[BEGIN]\n"),
], )),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
sanitized_mbpp_eval_cfg = dict(evaluator=dict(type=MBPPPassKEvaluator), pred_role="BOT")
sanitized_mbpp_datasets = [
dict(
type=SanitizedMBPPDataset,
abbr='sanitized_mbpp',
path='./sanitized-mbpp.jsonl',
reader_cfg=sanitized_mbpp_reader_cfg,
infer_cfg=sanitized_mbpp_infer_cfg,
eval_cfg=sanitized_mbpp_eval_cfg)
]

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configs/datasets/mbpp/sanitized_mbpp_repeat10_gen_1e1056.py Normal file
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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import SanitizedMBPPDataset, MBPPPassKEvaluator
sanitized_mbpp_reader_cfg = dict(
input_columns=['text', 'test_list'], output_column='test_column')
sanitized_mbpp_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(
round=[
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the similar elements from the given two tuple lists. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a python function to identify non-prime numbers. Your code should pass these tests:\n\n assert is_not_prime(2) == False \n assert is_not_prime(10) == True \n assert is_not_prime(35) == True \n"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: Write a function to find the largest integers from a given list of numbers using heap queue algorithm. Your code should pass these tests:\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"
),
dict(
role="BOT",
prompt=
"[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 "
),
dict(
role="HUMAN",
prompt=
"You are an expert Python programmer, and here is your task: {text} Your code should pass these tests:\n\n {test_list} \n"
),
dict(role="BOT", prompt="[BEGIN]\n"),
], )),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
sanitized_mbpp_eval_cfg = dict(evaluator=dict(type=MBPPPassKEvaluator), pred_role="BOT")
sanitized_mbpp_datasets = [
dict(
type=SanitizedMBPPDataset,
abbr='sanitized_mbpp_pass10',
path='./sanitized-mbpp.jsonl',
num_repeats=10,
reader_cfg=sanitized_mbpp_reader_cfg,
infer_cfg=sanitized_mbpp_infer_cfg,
eval_cfg=sanitized_mbpp_eval_cfg)
]

11
configs/eval_code_passk.py
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@ -6,19 +6,16 @@ from opencompass.models import HuggingFaceCausalLM
from opencompass.runners import LocalRunner
from opencompass.partitioners import SizePartitioner
from opencompass.tasks import OpenICLInferTask
from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator
with read_base():
from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
from .datasets.mbpp.mbpp_gen_1e1056 import mbpp_datasets
mbpp_datasets[0]['type'] = MBPPDataset_V2
mbpp_datasets[0]['eval_cfg']['evaluator']['type'] = MBPPPassKEvaluator
mbpp_datasets[0]['reader_cfg']['output_column'] = 'test_column'
from .datasets.humaneval.humaneval_passk_gen_8e312c import humaneval_datasets
from .datasets.mbpp.mbpp_passk_gen_1e1056 import mbpp_datasets
from .datasets.mbpp.sanitized_mbpp_passk_gen_1e1056 import sanitized_mbpp_datasets
datasets = []
datasets += humaneval_datasets
datasets += mbpp_datasets
datasets += sanitized_mbpp_datasets
models = [
dict(

15
configs/eval_code_passk_repeat_dataset.py
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@ -6,23 +6,16 @@ from opencompass.models import HuggingFaceCausalLM
from opencompass.runners import LocalRunner
from opencompass.partitioners import SizePartitioner
from opencompass.tasks import OpenICLInferTask
from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator
with read_base():
from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
from .datasets.mbpp.mbpp_gen_1e1056 import mbpp_datasets
humaneval_datasets[0]['abbr'] = 'openai_humaneval_pass10'
humaneval_datasets[0]['num_repeats'] = 10
mbpp_datasets[0]['abbr'] = 'mbpp_pass10'
mbpp_datasets[0]['num_repeats'] = 10
mbpp_datasets[0]['type'] = MBPPDataset_V2
mbpp_datasets[0]['eval_cfg']['evaluator']['type'] = MBPPPassKEvaluator
mbpp_datasets[0]['reader_cfg']['output_column'] = 'test_column'
from .datasets.humaneval.humaneval_repeat10_gen_8e312c import humaneval_datasets
from .datasets.mbpp.mbpp_repeat10_gen_1e1056 import mbpp_datasets
from .datasets.mbpp.sanitized_mbpp_repeat10_gen_1e1056 import sanitized_mbpp_datasets
datasets = []
datasets += humaneval_datasets
datasets += mbpp_datasets
datasets += sanitized_mbpp_datasets
_meta_template = dict(
round=[

36
opencompass/datasets/mbpp.py
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@ -71,6 +71,42 @@ class MBPPDataset_V2(BaseDataset):
return DatasetDict({'train': train, 'test': test})
class SanitizedMBPPDataset(BaseDataset):
@staticmethod
def load(path: str, num_repeats: int = 1):
"""Load mbpp dataset for pass k mode.
Note that you can use num_repeats > 1 when your model does not support
`num_return_sequence` in generation, otherwise use the raw
mbpp dataset and set `num_return_sequence` in model config to
generate multiple responses for testing pass@k>1.
It better to change your dataset abbr correspondingly if you want to
change num_repeats>1, otherwise the number in
`.cache/dataset_size.json` might be inconsistent.
Args:
num_repeats(int): Number of repetition for this dataset to get
multiple responses in special cases.
"""
def processing_test(example):
example['text'] = example.pop('prompt')
example['test_list'] = '\n'.join(example['test_list'])
example['test_column'] = dict(test_list_2=example['test_list'],
task_id=example['task_id'])
return example
# train : test = 7 : 257
train = load_dataset('json', data_files=path,
split='train[:7]').map(processing_test)
test = load_dataset('json', data_files=path,
split='train[7:264]').map(processing_test)
test = concatenate_datasets([test] * num_repeats)
return DatasetDict({'train': train, 'test': test})
class TimeOutException(Exception):
pass