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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
problem_key: string
rollout_index: int64
arm: string
steer: string
label: string
committed: bool
correct: bool
hit_token_cap: bool
finish_reason: string
output_tokens: int64
max_tokens_used: int64
completion: string
exec_correct: bool
exec_error: string
exec_extract: string
to
{'problem_key': Value('string'), 'rollout_index': Value('int64'), 'arm': Value('string'), 'steer': Value('string'), 'label': Value('string'), 'committed': Value('bool'), 'correct': Value('bool'), 'hit_token_cap': Value('bool'), 'finish_reason': Value('string'), 'output_tokens': Value('int64'), 'max_tokens_used': Value('int64'), 'completion': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
problem_key: string
rollout_index: int64
arm: string
steer: string
label: string
committed: bool
correct: bool
hit_token_cap: bool
finish_reason: string
output_tokens: int64
max_tokens_used: int64
completion: string
exec_correct: bool
exec_error: string
exec_extract: string
to
{'problem_key': Value('string'), 'rollout_index': Value('int64'), 'arm': Value('string'), 'steer': Value('string'), 'label': Value('string'), 'committed': Value('bool'), 'correct': Value('bool'), 'hit_token_cap': Value('bool'), 'finish_reason': Value('string'), 'output_tokens': Value('int64'), 'max_tokens_used': Value('int64'), 'completion': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
problem_key string | rollout_index int64 | arm string | steer string | label string | committed bool | correct bool | hit_token_cap bool | finish_reason string | output_tokens int64 | max_tokens_used int64 | completion string |
|---|---|---|---|---|---|---|---|---|---|---|---|
HumanEval/0 | 0 | none | base | run | false | false | false | stop | 996 | 31,744 | ```python
Okay, I need to write a Python function called has_close_elements that checks if there are any two numbers in the list that are closer to each other than a given threshold. Let's think about how to approach this.
First, the problem says that if any two numbers in the list have a difference less than the th... |
HumanEval/0 | 1 | none | base | run | false | false | false | stop | 780 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if there are any two numbers in a list that are closer than a given threshold. Let's think about how to approach this.
First, the problem says that if any two numbers in the list have an absolute difference less than the threshold, return... |
HumanEval/0 | 2 | none | base | run | false | false | false | stop | 1,284 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if there are any two numbers in a list that are closer to each other than a given threshold. Let's see. The examples given are helpful.
First, the first example: [1.0, 2.0, 3.0] with threshold 0.5. The differences between consecutive ele... |
HumanEval/0 | 3 | none | base | run | false | false | false | stop | 1,123 | 31,744 | The function has_close_elements takes a list of numbers and a threshold as input, and returns True if any two numbers in the list are closer to each other than the threshold, and False otherwise. The function can be implemented using a nested loop to compare each pair of numbers in the list and check if their differen... |
HumanEval/0 | 4 | none | base | run | false | false | false | stop | 943 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are closer to each other than a given threshold. Let's think about how to approach this.
First, the problem says that if there are two numbers in the list where the absolute difference between them is less tha... |
HumanEval/0 | 5 | none | base | run | false | false | false | stop | 1,717 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are within a certain threshold of each other. Let's see. The examples given: the first one returns False because the numbers are 1.0, 2.0, 3.0 with a threshold of 0.5. The differences between consecutive elemen... |
HumanEval/0 | 6 | none | base | run | false | false | false | stop | 1,406 | 31,744 | ERROR: type should be string, got " https://drive.google.com/file/d/1Fq9lP3L2Y9yj9k4I1b4YB7b9YpK1R4hK/view?usp=sharing\n\nOkay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are closer to each other than a given threshold. Let's think about how to approach this.\n\nFirst, the problem says that two numbers are considered close if their absolute difference is less than the threshold. So for every pair of numbers in the list, I need to check if the absolute difference is less than the threshold. If any such pair exists, return True; otherwise, False.\n\nBut checking all possible pairs might be inefficient, especially for large lists. Wait, but maybe the list isn't that big. The sample inputs have small lists, but the problem doesn't specify constraints on the input size. However, for the purposes of this problem, perhaps a straightforward approach is acceptable.\n\nSo the steps I need to take are:\n\n1. Iterate through each pair of numbers in the list.\n2. For each pair, calculate the absolute difference.\n3. If any difference is less than the threshold, return True immediately.\n4. If after checking all pairs no such pair is found, return False.\n\nBut how do I generate all possible pairs? One way is to use two nested loops. For each element at index i, compare it with every element at index j where j > i. That way, each pair is checked once.\n\nWait, but even comparing i and j where j is any index greater than i would cover all possible pairs without duplicates. Because (i,j) and (j,i) are the same pair. So using two loops where the inner loop starts from i+1 would avoid redundant checks. That's more efficient.\n\nSo the algorithm would be:\n\nLoop over each index i from 0 to len(numbers)-1:\n Loop over each index j from i+1 to len(numbers)-1:\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\nIf all pairs are checked and none meet the condition, return False.\n\nYes, that makes sense. Now, let's think about edge cases.\n\nWhat if the list has less than two elements? Then there are no pairs, so return False. For example, if the list is empty or has one element, the function should return False.\n\nAnother edge case: if the threshold is zero. Then the function should check if any two numbers are exactly equal. But the problem says \"closer than the threshold\", which would require the difference to be less than the threshold. So if threshold is zero, the only way two numbers are considered close is if they are equal. But if two numbers are exactly equal, their difference is zero, which is less than zero? Wait, no. Because zero is not less than zero. So if threshold is zero, the function should return False even if there are duplicates. Because the difference is zero, which is not less than zero. So in that case, the function would return False. For example, if the list is [1.0, 1.0], threshold 0.0, then the difference is 0.0, which is not less than 0.0, so returns False.\n\nAnother case: if the threshold is a very small positive number. Then even numbers that are very close but not exactly equal would be considered close.\n\nSo the code needs to handle all these cases.\n\nNow, implementing this in Python.\n\nThe function is given a list of floats and a threshold. So the parameters are numbers: List[float], threshold: float.\n\nFirst, check if the list has at least two elements. If not, return False.\n\nOtherwise, loop through each pair.\n\nSo the code structure would be:\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n n = len(numbers)\n if n < 2:\n return False\n for i in range(n):\n for j in range(i+1, n):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False\n\nWait, but what about the sample inputs?\n\nSample 1: [1.0, 2.0, 3.0], threshold 0.5. The differences are 1.0, 2.0, 1.0. All are >=0.5. So returns False. Correct.\n\nSample 2: [1.0, 2.8, 3.0, 4.0, 5.0, 2.0], threshold 0.3. Let's see. The pairs to check include 2.8 and 2.0. Their difference is 0.8, which is larger than 0.3. Wait, but maybe there's another pair. Let's see: 2.0 and 2.8? No, that's 0.8. Wait, maybe 3.0 and 2.8? 0.2, which is less than 0.3. So that pair would trigger the return True. So the code would return True. Which matches the sample.\n\nSo the code should work.\n\nBut what about the order of the loops? For example, in the sample, the code would check 2.8 and 3.0. The difference is 0.2, which is less than 0.3. So the code returns True. Correct.\n\nSo the code seems to handle the sample cases.\n\nAnother test case: if the list is [1.0, 1.0], threshold 0.0. The difference is 0.0, which is not less than 0.0. So returns False. Correct.\n\nAnother test case: [1.0, 1.1], threshold 0.05. The difference is 0.1, which is 0.1 >=0.05? No, 0.1 is 0.1, which is larger than 0.05. So returns False. Wait, 0.1 is larger than 0.05, so the condition is not met. So returns False. But if the threshold is 0.2, then 0.1 <0.2, so returns True.\n\nSo the code works.\n\nSo the code should be correct.```python\nfrom typing import List\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n n = len(numbers)\n if n < 2:\n return False\n for i in range(n):\n for j in range(i + 1, n):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False\n```" |
HumanEval/0 | 7 | none | base | run | false | false | false | stop | 1,083 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are closer to each other than a given threshold. Let's see.
The problem says that if there are any two elements in the list where their absolute difference is less than the threshold, then return True. Otherwi... |
HumanEval/0 | 8 | none | base | run | false | false | false | stop | 1,166 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are closer than a given threshold. Let's think about how to approach this.
First, the problem says that if there are two numbers in the list where the absolute difference between them is less than the threshol... |
HumanEval/0 | 9 | none | base | run | false | false | false | stop | 971 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are closer than a given threshold. Let's see.
First, the problem says that if there are any two elements in the list where the absolute difference is less than the threshold, return True. Otherwise, return Fal... |
HumanEval/0 | 10 | none | base | run | false | false | false | stop | 198 | 31,744 | ```python
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than
given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5)
False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
... |
HumanEval/0 | 11 | none | base | run | false | false | false | stop | 963 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if there are any two numbers in a list that are closer to each other than a given threshold. Let's think about how to approach this.
First, the problem says that the function should return True if any two elements in the list have an abso... |
HumanEval/0 | 12 | none | base | run | false | false | false | stop | 1,093 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if any two numbers in a list are closer to each other than a given threshold. Let's see. The examples given are like, for [1.0, 2.0, 3.0] with threshold 0.5, it returns False. But when the threshold is 0.3 and the list has 2.8 and 2.0, whi... |
HumanEval/0 | 13 | none | base | run | false | false | false | stop | 979 | 31,744 | ```python
Okay, I need to write a Python function called has_close_elements that checks if there are any two numbers in a list that are closer than a given threshold. Let's think about how to approach this.
First, the problem says that if any two numbers in the list are within the threshold of each other, the functi... |
HumanEval/0 | 14 | none | base | run | false | false | false | stop | 1,158 | 31,744 |
Okay, I need to write a Python function called has_close_elements that checks if there are any two numbers in a list that are closer than a given threshold. Let's think about how to approach this.
First, the problem says that any two numbers in the list must be checked. So for every pair of numbers in the list, I n... |
HumanEval/0 | 15 | none | base | run | false | false | false | stop | 1,538 | 31,744 | ERROR: type should be string, got " https://drive.google.com/file/d/1nXa5W8L6a7Q6q0V5Z2pW5B1R1N1N1N1/view?usp=sharing\n\nOkay, I need to write a Python function called has_close_elements that checks if there are any two numbers in a list that are closer than a given threshold. Let's think about how to approach this.\n\nFirst, the problem says that any two numbers in the list should be checked to see if their absolute difference is less than the threshold. So, the straightforward way is to compare every pair of numbers in the list and see if any pair's difference is below the threshold.\n\nBut wait, comparing every pair might be inefficient, especially for large lists. However, for the purpose of this problem, maybe the input lists aren't too big, so a brute-force approach is acceptable. Let's proceed with that.\n\nSo, the steps would be:\n\n1. Iterate through each pair of elements in the list.\n2. For each pair, calculate the absolute difference between the two numbers.\n3. If any of these differences is less than the threshold, return True immediately.\n4. If after checking all pairs no such pair is found, return False.\n\nBut how do I generate all possible pairs? Well, for a list of length n, there are n*(n-1)/2 possible pairs. To avoid checking the same pair twice (like (a, b) and (b, a)), I can loop with two indices where the first index is less than the second. So, for i in range(len(numbers)), and for j in range(i+1, len(numbers)), then check numbers[i] and numbers[j].\n\nYes, that's a good approach. So, the code would look something like:\n\nfor i in range(len(numbers)):\n for j in range(i+1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\nreturn False\n\nBut wait, what about the edge cases? For example, if the list has less than two elements, then there are no pairs, so the function should return False. So, first, we can check if the length of numbers is less than 2. If so, return False immediately. That's a good optimization.\n\nSo, the function would start by checking if len(numbers) < 2, return False. Then proceed with the loops.\n\nTesting the examples:\n\nFirst example: [1.0, 2.0, 3.0], threshold 0.5. The differences are 1.0 and 2.0, which are 1.0 and 1.0. Both are larger than 0.5, so return False. Correct.\n\nSecond example: [1.0, 2.8, 3.0, 4.0, 5.0, 2.0], threshold 0.3. Let's see. The elements 2.8 and 2.0 have a difference of 0.8, which is larger than 0.3. Wait, but maybe there's another pair. Let's check all pairs. For example, 2.8 and 3.0: difference is 0.2, which is less than 0.3. So the function should return True. So the code would find that pair and return True. So the example is correct.\n\nSo the code should work.\n\nNow, considering the data types. The list can have floats, which is handled by the abs function.\n\nSo putting it all together:\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n if len(numbers) < 2:\n return False\n for i in range(len(numbers)):\n for j in range(i+1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False\n\nThat should work. Let me test the sample inputs.\n\nSample 1: [1.0, 2.0, 3.0], threshold 0.5. The code checks 1 and 2 (diff 1.0), 1 and 3 (2.0), 2 and 3 (1.0). All are >=0.5. So returns False. Correct.\n\nSample 2: The list includes 2.8 and 3.0. Their difference is 0.2, which is less than 0.3. So returns True. Correct.\n\nAnother test case: empty list. Returns False. List with one element, returns False. All correct.\n\nWhat about if the threshold is zero? Then, the function checks if any two numbers are exactly equal. But since the problem says \"closer than threshold\", which is zero, so if two numbers are equal, their difference is zero, which is less than zero? Wait, no. If threshold is zero, then the condition is abs(a - b) < 0. But since absolute difference can't be negative, the only way this is true is if a and b are the same. Because 0 is not less than 0. So if threshold is zero, the function returns True only if there are two equal elements. For example, [1.0, 1.0, 3.0], threshold 0.0: returns True. Because 1.0 and 1.0 have a difference of 0.0, which is not less than 0.0. Wait, no. 0.0 is not less than 0.0. So the code would return False. Wait, that's a problem.\n\nWait, the problem statement says \"closer to each other than given threshold\". So, the threshold is the maximum allowed difference. So, if the threshold is 0.3, then any pair with difference less than 0.3 is considered close. So, if two elements are equal, their difference is zero, which is less than any positive threshold. So, in the case where threshold is zero, the code would return True only if there are two elements that are exactly equal. Because their difference is zero, which is not less than zero. So, for example, if the list is [1.0, 1.0], threshold 0.0, the code would check if 0.0 < 0.0, which is false. So returns False. But according to the problem statement, if the threshold is zero, then the function should return True only if there are two elements that are exactly the same. Because their difference is zero, which is not less than zero. So the code is correct in that case.\n\nSo the code handles that correctly.\n\nAnother test case: numbers = [5.0, 5.0], threshold = 0.0. The code checks the pair, difference is 0.0, which is not less than 0.0. So returns False. Which is correct.\n\nSo the code is correct.\n\nTherefore, the function should work as required.```python\nfrom typing import List\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n if len(numbers) < 2:\n return False\n for i in range(len(numbers)):\n for j in range(i + 1, len(numbers)):\n if abs(numbers[i] - numbers[j]) < threshold:\n return True\n return False\n```" |
HumanEval/1 | 0 | none | base | run | false | false | false | stop | 1,918 | 31,744 |
Okay, I need to solve this problem where I have to separate groups of parentheses from a string. Let's see. The input is a string with multiple groups, possibly with spaces, and I have to split them into individual strings. The examples show that even if there are spaces, they are ignored. So first, I should probabl... |
HumanEval/1 | 1 | none | base | run | false | false | false | stop | 1,311 | 31,744 |
Okay, I need to solve this problem where I have to separate groups of parentheses from a given string. The input is a string with multiple groups, possibly with spaces, and I have to split them into individual strings. Let's think about how to approach this.
First, the problem says that each group is balanced and n... |
HumanEval/1 | 2 | none | base | run | false | false | false | stop | 1,686 | 31,744 | ```python
Okay, I need to solve this problem where I have to separate groups of nested parentheses into individual strings. Let's see. The input is a string with multiple groups, possibly with spaces. The output should be a list of those groups, each properly balanced and not nested. Also, ignore spaces in the input.... |
HumanEval/1 | 3 | none | base | run | false | false | false | stop | 236 | 31,744 |
```python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
# Remove spaces from the input string
paren_string = paren_string.replace(" ", "")
# Initialize variables
stack = []
groups = []
current_group = ""
# Iterate through the characters in the strin... |
End of preview.
rollouts-olmo32b-cue — evaluation rollouts
Model: GRPO cue-forced ck300, merged from allenai/Olmo-3-1125-32B (adapters:
ReasoningRegisters/olmo32b-cue; every training rollout prefilled with ".\n\nOkay,").
Tokenizer used for answer positions: allenai/Olmo-3-1125-32B.
Protocol: 32 rollouts per problem (two seeded batches of 16), temperature 0.6, top-p 0.95,
budget 31,744 generated tokens, seed 20260819. Prompts and grader: the paper's repository
(sophicle/reason). Rollout jsonl files are kept as written under the run directories as on
disk (run/, run_execgraded/); cell summaries are at <prompt>/<benchmark>/summary/<arm>.json.
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