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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 0.003480 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._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 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2015, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1161, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 414, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Float value 0.003480 was truncated converting to int64
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.
secs int64 | km int64 | power int64 | hr int64 | cad int64 | alt float64 |
|---|---|---|---|---|---|
0 | 0 | 189 | 113 | 88 | 2.4 |
1 | 0 | 168 | 114 | 90 | 2.4 |
2 | 0 | 184 | 116 | 92 | 2.4 |
3 | 0 | 169 | 117 | 94 | 2.4 |
4 | 0 | 172 | 119 | 94 | 2.4 |
5 | 0 | 172 | 120 | 94 | 2.4 |
6 | 0 | 164 | 121 | 96 | 2.2 |
7 | 0 | 176 | 122 | 96 | 2.2 |
8 | 0 | 168 | 123 | 96 | 2.2 |
9 | 0 | 168 | 123 | 97 | 2.2 |
10 | 0 | 173 | 124 | 98 | 2.2 |
11 | 0 | 172 | 126 | 98 | 2.2 |
12 | 0 | 170 | 127 | 98 | 2.2 |
13 | 0 | 186 | 128 | 98 | 2.2 |
14 | 0 | 186 | 129 | 97 | 2.2 |
15 | 0 | 121 | 130 | 76 | 2.2 |
16 | 0 | 231 | 130 | 81 | 2.2 |
17 | 0 | 258 | 132 | 84 | 2 |
18 | 0 | 254 | 133 | 86 | 2 |
19 | 0 | 237 | 134 | 87 | 2 |
20 | 0 | 222 | 135 | 88 | 2 |
21 | 0 | 210 | 136 | 88 | 2 |
22 | 0 | 224 | 137 | 89 | 2 |
23 | 0 | 219 | 137 | 89 | 2 |
24 | 0 | 219 | 138 | 90 | 2.2 |
25 | 0 | 189 | 139 | 90 | 2.2 |
26 | 0 | 240 | 139 | 90 | 2.2 |
27 | 0 | 203 | 140 | 91 | 2.2 |
28 | 0 | 190 | 140 | 90 | 2.2 |
29 | 0 | 235 | 140 | 91 | 2.2 |
30 | 0 | 225 | 141 | 91 | 2.2 |
31 | 0 | 196 | 141 | 91 | 2.2 |
32 | 0 | 214 | 142 | 91 | 2.2 |
33 | 0 | 191 | 142 | 91 | 2.2 |
34 | 0 | 224 | 142 | 92 | 2.2 |
35 | 0 | 203 | 142 | 92 | 2.2 |
36 | 0 | 183 | 142 | 92 | 2.4 |
37 | 0 | 207 | 143 | 91 | 2.4 |
38 | 0 | 213 | 144 | 91 | 2.4 |
39 | 0 | 198 | 145 | 91 | 2.4 |
40 | 0 | 198 | 145 | 92 | 2.4 |
41 | 0 | 186 | 147 | 91 | 2.4 |
42 | 0 | 186 | 147 | 91 | 2.4 |
43 | 0 | 201 | 148 | 90 | 2.4 |
44 | 0 | 226 | 148 | 91 | 2.2 |
45 | 0 | 176 | 149 | 91 | 2.2 |
46 | 0 | 183 | 149 | 90 | 2.2 |
47 | 0 | 184 | 149 | 90 | 2.2 |
48 | 0 | 184 | 150 | 90 | 2.2 |
49 | 0 | 181 | 150 | 90 | 2.2 |
50 | 0 | 181 | 150 | 90 | 2.2 |
51 | 0 | 178 | 151 | 89 | 2.2 |
52 | 0 | 176 | 151 | 89 | 2.2 |
53 | 0 | 190 | 152 | 89 | 2.2 |
54 | 0 | 187 | 152 | 89 | 2.2 |
55 | 0 | 188 | 153 | 89 | 2.2 |
56 | 0 | 171 | 153 | 89 | 2.2 |
57 | 0 | 179 | 154 | 89 | 2.2 |
58 | 0 | 188 | 154 | 90 | 2.2 |
59 | 0 | 190 | 154 | 90 | 2.2 |
60 | 0 | 159 | 155 | 90 | 2.2 |
61 | 0 | 178 | 155 | 90 | 2.2 |
62 | 0 | 186 | 155 | 90 | 2.2 |
63 | 0 | 169 | 156 | 90 | 2.2 |
64 | 0 | 182 | 156 | 90 | 2.2 |
65 | 0 | 182 | 156 | 90 | 2.2 |
66 | 0 | 175 | 156 | 90 | 2.2 |
67 | 0 | 191 | 156 | 90 | 2.2 |
68 | 0 | 175 | 156 | 90 | 2.2 |
69 | 0 | 180 | 156 | 90 | 2.2 |
70 | 0 | 184 | 156 | 91 | 2.2 |
71 | 0 | 179 | 156 | 91 | 2.2 |
72 | 0 | 161 | 155 | 90 | 2.2 |
73 | 0 | 168 | 155 | 90 | 2.2 |
74 | 0 | 176 | 154 | 90 | 2.2 |
75 | 0 | 185 | 154 | 90 | 2.2 |
76 | 0 | 174 | 153 | 90 | 2.2 |
77 | 0 | 158 | 153 | 90 | 2.2 |
78 | 0 | 204 | 153 | 91 | 2.2 |
79 | 0 | 152 | 154 | 91 | 2.2 |
80 | 0 | 157 | 154 | 91 | 2.2 |
81 | 0 | 172 | 154 | 92 | 2.2 |
82 | 0 | 193 | 154 | 93 | 2.2 |
83 | 0 | 209 | 153 | 94 | 2.2 |
84 | 0 | 172 | 153 | 94 | 2.2 |
85 | 0 | 144 | 153 | 94 | 2.2 |
86 | 0 | 135 | 153 | 93 | 2.2 |
87 | 0 | 158 | 153 | 93 | 2.2 |
88 | 0 | 142 | 153 | 93 | 2.2 |
89 | 0 | 173 | 154 | 93 | 2.2 |
90 | 0 | 160 | 154 | 93 | 2.2 |
91 | 0 | 160 | 154 | 92 | 2.2 |
92 | 0 | 171 | 153 | 91 | 2.2 |
93 | 0 | 164 | 153 | 91 | 2.2 |
94 | 0 | 187 | 153 | 91 | 2.2 |
95 | 0 | 171 | 153 | 92 | 2.2 |
96 | 0 | 168 | 152 | 92 | 2.2 |
97 | 0 | 179 | 152 | 92 | 2.2 |
98 | 0 | 171 | 152 | 92 | 2.2 |
99 | 0 | 167 | 152 | 93 | 2.2 |
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