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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
example_id: int64
question_hash: string
benchmark_name: string
ground_truth: string
direct_pred: string
direct_valid: bool
direct_correct: bool
direct_text: string
preferred_sequence: list<item: string>
child 0, item: string
base_messages: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
branches: list<item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int (... 382 chars omitted)
child 0, item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int64, initial (... 370 chars omitted)
child 0, example_id: int64
child 1, question_hash: string
child 2, sequence: list<item: string>
child 0, item: string
child 3, depth: int64
child 4, initial_draft: string
child 5, drafts: list<item: string>
child 0, item: string
child 6, final_pred: string
child 7, valid: bool
child 8, correct: bool
child 9, probe_text: string
child 10, trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
child 0, role: string
child 1, content: string
child 2, tool_ca
...
3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
child 0, role: string
child 1, tool_call_id: string
child 2, name: string
child 3, content: string
probe_text: string
final_pred: string
valid: bool
depth: int64
initial_draft: string
sequence: list<item: string>
child 0, item: string
drafts: list<item: string>
child 0, item: string
trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
child 0, role: string
child 1, content: string
child 2, tool_calls: list<item: struct<id: string, type: string, function: struct<name: string, arguments: string>>>
child 0, item: struct<id: string, type: string, function: struct<name: string, arguments: string>>
child 0, id: string
child 1, type: string
child 2, function: struct<name: string, arguments: string>
child 0, name: string
child 1, arguments: string
child 3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
child 0, role: string
child 1, tool_call_id: string
child 2, name: string
child 3, content: string
correct: bool
to
{'example_id': Value('int64'), 'question_hash': Value('string'), 'sequence': List(Value('string')), 'depth': Value('int64'), 'initial_draft': Value('string'), 'drafts': List(Value('string')), 'final_pred': Value('string'), 'valid': Value('bool'), 'correct': Value('bool'), 'probe_text': Value('string'), 'trajectory': List({'role': Value('string'), 'content': Value('string'), 'tool_calls': List({'id': Value('string'), 'type': Value('string'), 'function': {'name': Value('string'), 'arguments': Value('string')}}), 'tool_event': {'role': Value('string'), 'tool_call_id': Value('string'), 'name': Value('string'), 'content': 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 1816, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
example_id: int64
question_hash: string
benchmark_name: string
ground_truth: string
direct_pred: string
direct_valid: bool
direct_correct: bool
direct_text: string
preferred_sequence: list<item: string>
child 0, item: string
base_messages: list<item: struct<role: string, content: string>>
child 0, item: struct<role: string, content: string>
child 0, role: string
child 1, content: string
branches: list<item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int (... 382 chars omitted)
child 0, item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int64, initial (... 370 chars omitted)
child 0, example_id: int64
child 1, question_hash: string
child 2, sequence: list<item: string>
child 0, item: string
child 3, depth: int64
child 4, initial_draft: string
child 5, drafts: list<item: string>
child 0, item: string
child 6, final_pred: string
child 7, valid: bool
child 8, correct: bool
child 9, probe_text: string
child 10, trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
child 0, role: string
child 1, content: string
child 2, tool_ca
...
3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
child 0, role: string
child 1, tool_call_id: string
child 2, name: string
child 3, content: string
probe_text: string
final_pred: string
valid: bool
depth: int64
initial_draft: string
sequence: list<item: string>
child 0, item: string
drafts: list<item: string>
child 0, item: string
trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
child 0, role: string
child 1, content: string
child 2, tool_calls: list<item: struct<id: string, type: string, function: struct<name: string, arguments: string>>>
child 0, item: struct<id: string, type: string, function: struct<name: string, arguments: string>>
child 0, id: string
child 1, type: string
child 2, function: struct<name: string, arguments: string>
child 0, name: string
child 1, arguments: string
child 3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
child 0, role: string
child 1, tool_call_id: string
child 2, name: string
child 3, content: string
correct: bool
to
{'example_id': Value('int64'), 'question_hash': Value('string'), 'sequence': List(Value('string')), 'depth': Value('int64'), 'initial_draft': Value('string'), 'drafts': List(Value('string')), 'final_pred': Value('string'), 'valid': Value('bool'), 'correct': Value('bool'), 'probe_text': Value('string'), 'trajectory': List({'role': Value('string'), 'content': Value('string'), 'tool_calls': List({'id': Value('string'), 'type': Value('string'), 'function': {'name': Value('string'), 'arguments': Value('string')}}), 'tool_event': {'role': Value('string'), 'tool_call_id': Value('string'), 'name': Value('string'), 'content': 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 1683, 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 1869, 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.
example_id int64 | question_hash string | sequence list | depth int64 | initial_draft string | drafts list | final_pred string | valid bool | correct bool | probe_text string | trajectory list |
|---|---|---|---|---|---|---|---|---|---|---|
54 | f57746353646854e | [
"extractor"
] | 1 | D | [
"D",
"D"
] | D | true | true | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_54_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_ca... |
54 | f57746353646854e | [
"reasoner"
] | 1 | D | [
"D",
"D"
] | D | true | true | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_54_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_call... |
54 | f57746353646854e | [
"verifier"
] | 1 | D | [
"D",
"D"
] | D | true | true | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_54_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"D\"}"
}
}
],
"tool_event": {
"role": ... |
209 | 591d24dd6fdd73c8 | [
"extractor"
] | 1 | C | [
"C",
"C"
] | C | true | false | DRAFT_ANSWER_C
ANSWER_C | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_C",
"tool_calls": [
{
"id": "mv_209_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
209 | 591d24dd6fdd73c8 | [
"reasoner"
] | 1 | C | [
"C",
"C"
] | C | true | false | DRAFT_ANSWER_C
ANSWER_C | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_C",
"tool_calls": [
{
"id": "mv_209_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
209 | 591d24dd6fdd73c8 | [
"verifier"
] | 1 | C | [
"C",
"A"
] | A | true | true | DRAFT_ANSWER_A
ANSWER_A | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_C",
"tool_calls": [
{
"id": "mv_209_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"C\"}"
}
}
],
"tool_event": {
"role":... |
141 | 0df23053184378c3 | [
"extractor"
] | 1 | C | [
"C",
"C"
] | C | true | true | DRAFT_ANSWER_C
ANSWER_C | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_C",
"tool_calls": [
{
"id": "mv_141_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
141 | 0df23053184378c3 | [
"reasoner"
] | 1 | C | [
"C",
"C"
] | C | true | true | DRAFT_ANSWER_C
ANSWER_C | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_C",
"tool_calls": [
{
"id": "mv_141_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
141 | 0df23053184378c3 | [
"verifier"
] | 1 | C | [
"C",
"C"
] | C | true | true | DRAFT_ANSWER_C
ANSWER_C | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_C",
"tool_calls": [
{
"id": "mv_141_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"C\"}"
}
}
],
"tool_event": {
"role":... |
312 | 5672f6a2a612bee1 | [
"extractor"
] | 1 | D | [
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
312 | 5672f6a2a612bee1 | [
"reasoner"
] | 1 | D | [
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
312 | 5672f6a2a612bee1 | [
"verifier"
] | 1 | D | [
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"D\"}"
}
}
],
"tool_event": {
"role":... |
312 | 5672f6a2a612bee1 | [
"extractor",
"reasoner"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
312 | 5672f6a2a612bee1 | [
"extractor",
"verifier"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
312 | 5672f6a2a612bee1 | [
"reasoner",
"extractor"
] | 2 | D | [
"D",
"D",
"A"
] | A | true | true | DRAFT_ANSWER_A
ANSWER_A | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
312 | 5672f6a2a612bee1 | [
"reasoner",
"verifier"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
312 | 5672f6a2a612bee1 | [
"verifier",
"extractor"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"D\"}"
}
}
],
"tool_event": {
"role":... |
312 | 5672f6a2a612bee1 | [
"verifier",
"reasoner"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_312_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"D\"}"
}
}
],
"tool_event": {
"role":... |
317 | f54404edf31fcb59 | [
"extractor"
] | 1 | D | [
"D",
"D"
] | D | true | false | ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
317 | f54404edf31fcb59 | [
"reasoner"
] | 1 | D | [
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
317 | f54404edf31fcb59 | [
"verifier"
] | 1 | D | [
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"D\"}"
}
}
],
"tool_event": {
"role":... |
317 | f54404edf31fcb59 | [
"extractor",
"reasoner"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
317 | f54404edf31fcb59 | [
"extractor",
"verifier"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_extractor",
"type": "function",
"function": {
"name": "extractor_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_c... |
317 | f54404edf31fcb59 | [
"reasoner",
"extractor"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
317 | f54404edf31fcb59 | [
"reasoner",
"verifier"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_reasoner",
"type": "function",
"function": {
"name": "reasoner_tool",
"arguments": "{}"
}
}
],
"tool_event": {
"role": "tool",
"tool_cal... |
317 | f54404edf31fcb59 | [
"verifier",
"extractor"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
"id": "mv_317_verifier",
"type": "function",
"function": {
"name": "verifier_tool",
"arguments": "{\"current_draft\": \"D\"}"
}
}
],
"tool_event": {
"role":... |
317 | f54404edf31fcb59 | [
"verifier",
"reasoner"
] | 2 | D | [
"D",
"D",
"D"
] | D | true | false | DRAFT_ANSWER_D
ANSWER_D | [
{
"role": "assistant",
"content": "DRAFT_ANSWER_D",
"tool_calls": [
{
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The paramagnetic coupling term (Zeeman energy shift) is given by $\langle H \rangle = m \mu_B B$. For small $m$ (e.g., $m=1$) and $B=1$ T, this is approximately $1 \times 5.79 \times 10^{-5}$ eV $\approx 5.8 \times 10^{-5}$ eV. The transition energy $\Delta E$ for $\lambda = 0.4861 \mu$m (H$\beta$ line,... | [
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289 | 367f9dfee792233b | [
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289 | 367f9dfee792233b | [
"extractor",
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289 | 367f9dfee792233b | [
"extractor",
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289 | 367f9dfee792233b | [
"reasoner",
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