Datasets:

TemryL
/

LLM-Disease-Risk-Scores

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train

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Float value 68.977 was truncated converting to int64
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
                  arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
                  arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature
                  return array_cast(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1963, in array_cast
                  return array.cast(pa_type)
                File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
                  return call_function("cast", [arr], options, memory_pool)
                File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Float value 68.977 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 1529, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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config dict	eids sequence	y_true sequence	y_scores sequence
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0(...TRUNCATED)	[0.665587842464447,0.7243086695671082,0.5918289422988892,0.6475510597229004,0.6225112676620483,0.614(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0(...TRUNCATED)	[0.8752654194831848,0.9623286128044128,0.7720351815223694,0.8171884417533875,0.9268544316291809,0.94(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0(...TRUNCATED)	[0.708955705165863,0.7507752776145935,0.7534188628196716,0.7320746779441833,0.6825377345085144,0.725(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0(...TRUNCATED)	[0.9501780271530151,0.9196169972419739,0.906417965888977,0.8691039085388184,0.8704098463058472,0.944(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0(...TRUNCATED)	[0.8849499821662903,0.9058308601379395,0.8753970861434937,0.8931938409805298,0.8856927752494812,0.89(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0(...TRUNCATED)	[0.7575373649597168,0.8446401953697205,0.6978434324264526,0.7215484976768494,0.8640618324279785,0.92(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0(...TRUNCATED)	[0.865276575088501,0.8476865887641907,0.844234824180603,0.8350293636322021,0.8090861439704895,0.8544(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0(...TRUNCATED)	[0.9454464912414551,0.939940333366394,0.9490334391593933,0.9302350878715515,0.910264253616333,0.9758(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[1,1,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0(...TRUNCATED)	[0.7452203631401062,0.8363126516342163,0.672965407371521,0.7112821936607361,0.7918761372566223,0.830(...TRUNCATED)
{"model":"BioMistral/BioMistral-7B","base_model":"","revision":"main","precision":"float16","weight_(...TRUNCATED)	[1387471,3401115,1513894,3496238,2217230,3263301,3146362,1659371,3541047,2369948,3855441,4369004,105(...TRUNCATED)	[1,1,0,0,0,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0(...TRUNCATED)	[0.8436540365219116,0.8871424198150635,0.734725832939148,0.7838150262832642,0.8705726265907288,0.932(...TRUNCATED)

End of preview.