Datasets:

bongard0v0
/

curriculum_learning

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train

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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 ({'eval_loss', 'eval_runtime', 'eval_steps_per_second', 'eval_samples_per_second'}) and 5 missing columns ({'total_flos', 'train_runtime', 'train_loss', 'train_steps_per_second', 'train_samples_per_second'}).

This happened while the json dataset builder was generating data using

hf://datasets/bongard0v0/curriculum_learning/100_lora_sft_ttt/eval_results.json (at revision e838323843e7e131df3025169fb2372180c2c660)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              epoch: double
              eval_loss: double
              eval_runtime: double
              eval_samples_per_second: double
              eval_steps_per_second: double
              to
              {'epoch': Value('float64'), 'total_flos': Value('float64'), 'train_loss': Value('float64'), 'train_runtime': Value('float64'), 'train_samples_per_second': Value('float64'), 'train_steps_per_second': Value('float64')}
              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 1451, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, 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 1833, 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 ({'eval_loss', 'eval_runtime', 'eval_steps_per_second', 'eval_samples_per_second'}) and 5 missing columns ({'total_flos', 'train_runtime', 'train_loss', 'train_steps_per_second', 'train_samples_per_second'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/bongard0v0/curriculum_learning/100_lora_sft_ttt/eval_results.json (at revision e838323843e7e131df3025169fb2372180c2c660)
              
              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.

epoch float64	total_flos float64	train_loss float64	train_runtime float64	train_samples_per_second float64	train_steps_per_second float64
2	34,102,773,284,864	0.023134	4,154.1424	0.901	0.225
2	11,399,228,555,264	0.017152	3,811.5722	0.327	0.082
2	12,490,745,577,472	0.018073	3,749.7125	0.364	0.091
2	13,584,634,740,736	0.018395	4,146.6115	0.358	0.09
2	14,673,778,311,168	0.01712	4,997.1732	0.321	0.08
2	15,760,549,085,184	0.016697	5,747.7579	0.3	0.075
2	16,784,043,802,624	0.01747	5,132.4203	0.359	0.09
2	17,875,560,562,688	0.019762	6,086.1536	0.322	0.081
2	18,967,076,798,464	0.017179	5,342.1921	0.389	0.097
2	20,053,847,703,552	0.017113	4,979.6252	0.441	0.11
2	21,150,110,056,448	0.019086	2,564.3566	0.903	0.226
2	1,721,115,869,184	0.013244	545.1918	0.326	0.084
2	22,241,626,030,080	0.020467	5,021.3712	0.485	0.121
2	23,333,143,576,576	0.01766	2,890.1849	0.884	0.221
2	24,351,892,045,824	0.019903	3,084.4055	0.866	0.217
2	25,443,408,281,600	0.018505	3,187.7654	0.875	0.219
2	26,534,924,713,984	0.019952	3,289.8477	0.885	0.221
2	27,626,441,408,512	0.019229	3,748.5815	0.808	0.202
2	28,717,958,037,504	0.018041	4,128.7866	0.762	0.191
2	29,809,474,273,280	0.019256	4,513.7196	0.724	0.181
2	30,828,222,873,600	0.021105	5,346.9897	0.633	0.158
2	31,919,738,978,304	0.021841	7,275.7938	0.482	0.12
2	2,739,864,600,576	0.025909	1,038.259	0.285	0.071
2	33,011,256,262,656	0.018509	17,096.3961	0.212	0.053
2	34,102,773,284,864	0.019541	4,926.8156	0.76	0.19
2	34,102,773,284,864	0.019666	5,115.5056	0.732	0.183
2	34,102,773,284,864	0.02016	5,362.3013	0.698	0.175
2	34,102,773,284,864	0.019017	5,159.6985	0.725	0.181
2	34,102,773,284,864	0.019854	6,901.7333	0.542	0.136
2	34,102,773,284,864	0.021063	5,277.1907	0.709	0.177
2	34,102,773,284,864	0.021561	6,526.4536	0.573	0.143
2	34,102,773,284,864	0.018413	8,402.1185	0.445	0.111
2	34,102,773,284,864	0.017154	5,826.4036	0.642	0.161
2	3,831,381,098,496	0.031117	1,518.5428	0.273	0.068
2	34,102,773,284,864	0.020121	5,948.6805	0.629	0.157
2	34,102,773,284,864	0.020604	5,724.476	0.654	0.164
2	34,102,773,284,864	0.020017	5,513.8694	0.679	0.17
2	34,102,773,284,864	0.022908	6,639.0266	0.564	0.141
2	34,102,773,284,864	0.022631	5,372.172	0.697	0.174
2	34,102,773,284,864	0.022247	5,078.045	0.737	0.184
2	34,102,773,284,864	0.021007	5,195.6171	0.72	0.18
2	34,102,773,284,864	0.021825	4,410.1132	0.849	0.212
2	34,102,773,284,864	0.023767	7,121.3423	0.525	0.131
2	34,102,773,284,864	0.021629	4,510.4072	0.83	0.208
2	4,922,897,596,416	0.019774	2,072.2513	0.258	0.065
2	34,102,773,284,864	0.023129	4,331.9344	0.864	0.216
2	34,102,773,284,864	0.025288	4,833.955	0.774	0.194
2	34,102,773,284,864	0.025547	4,958.1427	0.755	0.189
2	34,102,773,284,864	0.023251	4,455.8048	0.84	0.21
2	34,102,773,284,864	0.027415	4,801.9968	0.779	0.195
2	34,102,773,284,864	0.025308	4,817.8593	0.777	0.194
2	34,102,773,284,864	0.027978	4,812.7973	0.778	0.194
2	34,102,773,284,864	0.024857	4,811.9952	0.778	0.195
2	34,102,773,284,864	0.027804	4,809.142	0.778	0.195
2	34,102,773,284,864	0.024356	4,614.0783	0.811	0.203
2	6,014,414,094,336	0.020427	2,327.2365	0.28	0.07
2	34,102,773,284,864	0.02548	4,590.3903	0.815	0.204
2	34,102,773,284,864	0.026831	5,037.8577	0.743	0.186
2	34,102,773,284,864	0.025005	5,100.4884	0.734	0.184
2	34,102,773,284,864	0.026258	5,015.4468	0.746	0.187
2	34,102,773,284,864	0.027284	5,151.5342	0.726	0.182
2	34,102,773,284,864	0.030436	5,044.7361	0.742	0.186
2	34,102,773,284,864	0.02558	4,401.4381	0.85	0.213
2	34,102,773,284,864	0.029475	4,991.2432	0.75	0.188
2	34,102,773,284,864	0.027894	5,067.2032	0.738	0.185
2	34,102,773,284,864	0.029373	5,157.4805	0.726	0.181
2	7,105,930,854,400	0.017938	1,902.1432	0.406	0.102
2	34,102,773,284,864	0.027415	5,256.2551	0.712	0.178
2	34,102,773,284,864	0.029488	5,132.8976	0.729	0.182
2	34,102,773,284,864	0.033775	5,105.4806	0.733	0.183
2	34,102,773,284,864	0.03098	5,117.0774	0.731	0.183
2	34,102,773,284,864	0.02981	5,114.7655	0.732	0.183
2	34,102,773,284,864	0.02912	5,108.1149	0.733	0.183
2	34,102,773,284,864	0.027585	5,126.5525	0.73	0.183
2	34,102,773,284,864	0.030835	5,135.4671	0.729	0.182
2	34,102,773,284,864	0.026749	5,295.8159	0.707	0.177
2	34,102,773,284,864	0.028083	4,987.4854	0.75	0.188
2	8,197,447,090,176	0.020428	2,455.8551	0.362	0.091
2	34,102,773,284,864	0.031003	4,456.7398	0.84	0.21
2	34,102,773,284,864	0.030035	5,133.6067	0.729	0.182
2	34,102,773,284,864	0.031316	5,070.8749	0.738	0.185
2	34,102,773,284,864	0.03007	5,193.9736	0.72	0.18
2	34,102,773,284,864	0.031715	4,157.6127	0.9	0.225
2	34,102,773,284,864	0.032485	4,224.3489	0.886	0.222
2	34,102,773,284,864	0.030077	4,161.4701	0.899	0.225
2	34,102,773,284,864	0.029293	4,277.6933	0.875	0.219
2	34,102,773,284,864	0.027377	4,438.9971	0.843	0.211
2	34,102,773,284,864	0.027512	4,140.1456	0.904	0.226
2	9,211,449,966,592	0.023144	2,342.9168	0.43	0.108
2	34,102,773,284,864	0.029027	4,491.549	0.833	0.208
2	34,102,773,284,864	0.027991	4,146.3641	0.902	0.226
2	34,102,773,284,864	0.026812	4,135.2879	0.905	0.226
2	34,102,773,284,864	0.027402	4,238.467	0.883	0.221
2	34,102,773,284,864	0.02622	4,345.9678	0.861	0.215
2	34,102,773,284,864	0.031219	4,264.682	0.877	0.219
2	34,102,773,284,864	0.027201	5,153.3615	0.726	0.182
2	34,102,773,284,864	0.024831	7,992.0951	0.468	0.117
2	34,102,773,284,864	0.026272	4,524.154	0.827	0.207
2	34,102,773,284,864	0.026412	4,278.4534	0.875	0.219
2	10,307,712,319,488	0.01629	3,650.5178	0.309	0.077

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