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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'summary_tasks', 'config_tasks', 'config_general', 'summary_general'}) and 3 missing columns ({'task_config', 'config', 'hashes'}).
This happened while the json dataset builder was generating data using
hf://datasets/errolseo/results/maywell/Synatra-7B-v0.3-base/results_2023-09-23T12-27-31.812773.json (at revision bfbfc0da70993d2c08334671c1d177b7ec41c3f2)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
config_general: struct<model_name: string, model_sha: string, model_size: string, model_dtype: string, lighteval_sha: string, num_few_shot_default: int64, num_fewshot_seeds: int64, override_batch_size: int64, max_samples: null, job_id: string>
child 0, model_name: string
child 1, model_sha: string
child 2, model_size: string
child 3, model_dtype: string
child 4, lighteval_sha: string
child 5, num_few_shot_default: int64
child 6, num_fewshot_seeds: int64
child 7, override_batch_size: int64
child 8, max_samples: null
child 9, job_id: string
results: struct<harness|drop|3: struct<em: double, em_stderr: double, f1: double, f1_stderr: double>, harness|gsm8k|5: struct<acc: double, acc_stderr: double>, harness|winogrande|5: struct<acc: double, acc_stderr: double>, all: struct<em: double, em_stderr: double, f1: double, f1_stderr: double, acc: double, acc_stderr: double>>
child 0, harness|drop|3: struct<em: double, em_stderr: double, f1: double, f1_stderr: double>
child 0, em: double
child 1, em_stderr: double
child 2, f1: double
child 3, f1_stderr: double
child 1, harness|gsm8k|5: struct<acc: double, acc_stderr: double>
child 0, acc: double
child 1, acc_stderr: double
child 2, harness|winogrande|5: struct<acc: double, acc_stderr: double>
child 0, acc: double
child 1, acc_stderr: double
child 3, all: struct<em: double, em_stderr: double, f1: double, f1_stderr: double, acc: double, acc_stderr: double>
chi
...
, hash_input_tokens: string, hash_cont_tokens: string>, truncated: int64, non-truncated: int64, padded: int64, non-padded: int64, effective_few_shots: double, num_truncated_few_shots: int64>
child 0, hashes: struct<hash_examples: string, hash_full_prompts: string, hash_input_tokens: string, hash_cont_tokens: string>
child 0, hash_examples: string
child 1, hash_full_prompts: string
child 2, hash_input_tokens: string
child 3, hash_cont_tokens: string
child 1, truncated: int64
child 2, non-truncated: int64
child 3, padded: int64
child 4, non-padded: int64
child 5, effective_few_shots: double
child 6, num_truncated_few_shots: int64
summary_general: struct<hashes: struct<hash_examples: string, hash_full_prompts: string, hash_input_tokens: string, hash_cont_tokens: string>, total_evaluation_time_secondes: string, truncated: int64, non-truncated: int64, padded: int64, non-padded: int64, num_truncated_few_shots: int64>
child 0, hashes: struct<hash_examples: string, hash_full_prompts: string, hash_input_tokens: string, hash_cont_tokens: string>
child 0, hash_examples: string
child 1, hash_full_prompts: string
child 2, hash_input_tokens: string
child 3, hash_cont_tokens: string
child 1, total_evaluation_time_secondes: string
child 2, truncated: int64
child 3, non-truncated: int64
child 4, padded: int64
child 5, non-padded: int64
child 6, num_truncated_few_shots: int64
to
{'results': {'harness|arc:challenge|25': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='float64', id=None), 'acc_norm_stderr': Value(dtype='float64', id=None)}, 'harness|hellaswag|10': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='float64', id=None), 'acc_norm_stderr': Value(dtype='float64', id=None)}, 'harness|hendrycksTest-abstract_algebra|5': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='float64', id=None), 'acc_norm_stderr': Value(dtype='float64', id=None)}, 'harness|hendrycksTest-anatomy|5': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='float64', id=None), 'acc_norm_stderr': Value(dtype='float64', id=None)}, 'harness|hendrycksTest-astronomy|5': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='float64', id=None), 'acc_norm_stderr': Value(dtype='float64', id=None)}, 'harness|hendrycksTest-business_ethics|5': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='float64', id=None), 'acc_norm_stderr': Value(dtype='float64', id=None)}, 'harness|hendrycksTest-clinical_knowledge|5': {'acc': Value(dtype='float64', id=None), 'acc_stderr': Value(dtype='float64', id=None), 'acc_norm': Value(dtype='f
...
', id=None)}, 'harness|hendrycksTest-security_studies|5': {'hash_examples': Value(dtype='string', id=None), 'hash_full_prompts': Value(dtype='string', id=None), 'hash_input_tokens': Value(dtype='string', id=None), 'hash_cont_tokens': Value(dtype='string', id=None)}, 'harness|hendrycksTest-sociology|5': {'hash_examples': Value(dtype='string', id=None), 'hash_full_prompts': Value(dtype='string', id=None), 'hash_input_tokens': Value(dtype='string', id=None), 'hash_cont_tokens': Value(dtype='string', id=None)}, 'harness|hendrycksTest-us_foreign_policy|5': {'hash_examples': Value(dtype='string', id=None), 'hash_full_prompts': Value(dtype='string', id=None), 'hash_input_tokens': Value(dtype='string', id=None), 'hash_cont_tokens': Value(dtype='string', id=None)}, 'harness|hendrycksTest-virology|5': {'hash_examples': Value(dtype='string', id=None), 'hash_full_prompts': Value(dtype='string', id=None), 'hash_input_tokens': Value(dtype='string', id=None), 'hash_cont_tokens': Value(dtype='string', id=None)}, 'harness|hendrycksTest-world_religions|5': {'hash_examples': Value(dtype='string', id=None), 'hash_full_prompts': Value(dtype='string', id=None), 'hash_input_tokens': Value(dtype='string', id=None), 'hash_cont_tokens': Value(dtype='string', id=None)}, 'harness|truthfulqa:mc|0': {'hash_examples': Value(dtype='string', id=None), 'hash_full_prompts': Value(dtype='string', id=None), 'hash_input_tokens': Value(dtype='string', id=None), 'hash_cont_tokens': Value(dtype='string', id=None)}}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, 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 935, 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 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'summary_tasks', 'config_tasks', 'config_general', 'summary_general'}) and 3 missing columns ({'task_config', 'config', 'hashes'}).
This happened while the json dataset builder was generating data using
hf://datasets/errolseo/results/maywell/Synatra-7B-v0.3-base/results_2023-09-23T12-27-31.812773.json (at revision bfbfc0da70993d2c08334671c1d177b7ec41c3f2)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
results
dict | versions
dict | config
dict | task_config
dict | hashes
dict | config_general
dict | config_tasks
dict | summary_tasks
dict | summary_general
dict |
|---|---|---|---|---|---|---|---|---|
{
"harness|arc:challenge|25": {
"acc": 0.514505119453925,
"acc_stderr": 0.014605241081370053,
"acc_norm": 0.35545,
"acc_norm_stderr": 0.01451842182567044
},
"harness|hellaswag|10": {
"acc": 0.5948018323043218,
"acc_stderr": 0.004899270310557987,
"acc_norm": 0.7924716191993627,
"acc_norm_stderr": 0.00404708312009885
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45185185185185184,
"acc_stderr": 0.04299268905480864,
"acc_norm": 0.45185185185185184,
"acc_norm_stderr": 0.04299268905480864
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4934210526315789,
"acc_stderr": 0.040685900502249704,
"acc_norm": 0.4934210526315789,
"acc_norm_stderr": 0.040685900502249704
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5132075471698113,
"acc_stderr": 0.030762134874500482,
"acc_norm": 0.5132075471698113,
"acc_norm_stderr": 0.030762134874500482
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.041795966175810016,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.041795966175810016
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4797687861271676,
"acc_stderr": 0.03809342081273958,
"acc_norm": 0.4797687861271676,
"acc_norm_stderr": 0.03809342081273958
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3574468085106383,
"acc_stderr": 0.03132941789476425,
"acc_norm": 0.3574468085106383,
"acc_norm_stderr": 0.03132941789476425
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.34210526315789475,
"acc_stderr": 0.04462917535336937,
"acc_norm": 0.34210526315789475,
"acc_norm_stderr": 0.04462917535336937
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4413793103448276,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.4413793103448276,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2724867724867725,
"acc_stderr": 0.022930973071633345,
"acc_norm": 0.2724867724867725,
"acc_norm_stderr": 0.022930973071633345
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30158730158730157,
"acc_stderr": 0.041049472699033945,
"acc_norm": 0.30158730158730157,
"acc_norm_stderr": 0.041049472699033945
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5548387096774193,
"acc_stderr": 0.028272410186214906,
"acc_norm": 0.5548387096774193,
"acc_norm_stderr": 0.028272410186214906
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.33497536945812806,
"acc_stderr": 0.033208527423483104,
"acc_norm": 0.33497536945812806,
"acc_norm_stderr": 0.033208527423483104
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6121212121212121,
"acc_stderr": 0.038049136539710114,
"acc_norm": 0.6121212121212121,
"acc_norm_stderr": 0.038049136539710114
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6565656565656566,
"acc_stderr": 0.033832012232444426,
"acc_norm": 0.6565656565656566,
"acc_norm_stderr": 0.033832012232444426
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6632124352331606,
"acc_stderr": 0.03410780251836184,
"acc_norm": 0.6632124352331606,
"acc_norm_stderr": 0.03410780251836184
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.47692307692307695,
"acc_stderr": 0.025323990861736125,
"acc_norm": 0.47692307692307695,
"acc_norm_stderr": 0.025323990861736125
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2740740740740741,
"acc_stderr": 0.027195934804085622,
"acc_norm": 0.2740740740740741,
"acc_norm_stderr": 0.027195934804085622
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.4957983193277311,
"acc_stderr": 0.03247734334448111,
"acc_norm": 0.4957983193277311,
"acc_norm_stderr": 0.03247734334448111
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2781456953642384,
"acc_stderr": 0.03658603262763743,
"acc_norm": 0.2781456953642384,
"acc_norm_stderr": 0.03658603262763743
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6825688073394496,
"acc_stderr": 0.019957152198460493,
"acc_norm": 0.6825688073394496,
"acc_norm_stderr": 0.019957152198460493
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4212962962962963,
"acc_stderr": 0.03367462138896078,
"acc_norm": 0.4212962962962963,
"acc_norm_stderr": 0.03367462138896078
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6323529411764706,
"acc_stderr": 0.03384132045674119,
"acc_norm": 0.6323529411764706,
"acc_norm_stderr": 0.03384132045674119
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6708860759493671,
"acc_stderr": 0.030587326294702368,
"acc_norm": 0.6708860759493671,
"acc_norm_stderr": 0.030587326294702368
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5067264573991032,
"acc_stderr": 0.033554765962343545,
"acc_norm": 0.5067264573991032,
"acc_norm_stderr": 0.033554765962343545
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6335877862595419,
"acc_stderr": 0.042258754519696365,
"acc_norm": 0.6335877862595419,
"acc_norm_stderr": 0.042258754519696365
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6776859504132231,
"acc_stderr": 0.04266416363352168,
"acc_norm": 0.6776859504132231,
"acc_norm_stderr": 0.04266416363352168
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5648148148148148,
"acc_stderr": 0.04792898170907061,
"acc_norm": 0.5648148148148148,
"acc_norm_stderr": 0.04792898170907061
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5644171779141104,
"acc_stderr": 0.03895632464138937,
"acc_norm": 0.5644171779141104,
"acc_norm_stderr": 0.03895632464138937
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.32142857142857145,
"acc_stderr": 0.0443280405529152,
"acc_norm": 0.32142857142857145,
"acc_norm_stderr": 0.0443280405529152
},
"harness|hendrycksTest-management|5": {
"acc": 0.6699029126213593,
"acc_stderr": 0.0465614711001235,
"acc_norm": 0.6699029126213593,
"acc_norm_stderr": 0.0465614711001235
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7606837606837606,
"acc_stderr": 0.027951826808924333,
"acc_norm": 0.7606837606837606,
"acc_norm_stderr": 0.027951826808924333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6615581098339719,
"acc_stderr": 0.01692086958621066,
"acc_norm": 0.6615581098339719,
"acc_norm_stderr": 0.01692086958621066
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5173410404624278,
"acc_stderr": 0.02690290045866664,
"acc_norm": 0.5173410404624278,
"acc_norm_stderr": 0.02690290045866664
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24692737430167597,
"acc_stderr": 0.01442229220480884,
"acc_norm": 0.24692737430167597,
"acc_norm_stderr": 0.01442229220480884
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5718954248366013,
"acc_stderr": 0.028332397483664278,
"acc_norm": 0.5718954248366013,
"acc_norm_stderr": 0.028332397483664278
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5498392282958199,
"acc_stderr": 0.028256660723360173,
"acc_norm": 0.5498392282958199,
"acc_norm_stderr": 0.028256660723360173
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.02780165621232366,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.02780165621232366
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.32978723404255317,
"acc_stderr": 0.0280459469420424,
"acc_norm": 0.32978723404255317,
"acc_norm_stderr": 0.0280459469420424
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3956975228161669,
"acc_stderr": 0.01248929073544901,
"acc_norm": 0.3956975228161669,
"acc_norm_stderr": 0.01248929073544901
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5257352941176471,
"acc_stderr": 0.030332578094555033,
"acc_norm": 0.5257352941176471,
"acc_norm_stderr": 0.030332578094555033
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4526143790849673,
"acc_stderr": 0.020136790918492534,
"acc_norm": 0.4526143790849673,
"acc_norm_stderr": 0.020136790918492534
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5272727272727272,
"acc_stderr": 0.04782001791380061,
"acc_norm": 0.5272727272727272,
"acc_norm_stderr": 0.04782001791380061
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5714285714285714,
"acc_stderr": 0.03168091161233882,
"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.03168091161233882
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.681592039800995,
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