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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 8 new columns ({'valid/reg_loss_5/dataloader_idx_0', 'train/reg_loss_5', 'valid/reg_loss_4/dataloader_idx_0', 'valid/pred_loss_5/dataloader_idx_0', 'valid/pred_loss_4/dataloader_idx_0', 'train/pred_loss_4', 'train/pred_loss_5', 'train/reg_loss_4'})
This happened while the csv dataset builder was generating data using
hf://datasets/MLDS-NUS/Meso-SpecOnsNet/logs/official/runs/ckdv/fno/csv/version_0/metrics.csv (at revision ce28d024519508f1e8145e408caa97f3e5334ded), ['hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/ac/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/ac_realV/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/s_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d/csv/version_1/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d_realV/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d_realV/csv/version_1/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/res_onsagernet_2d/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_2/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_4/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_5/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_6/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/s_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/s_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/kdv/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/kdv_realV/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/s_onsagernet/csv/version_0/metrics.csv']
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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
epoch: double
lr-Adam: double
step: int64
train/loss: double
train/pred_loss: double
train/pred_loss_1: double
train/pred_loss_2: double
train/pred_loss_3: double
train/pred_loss_4: double
train/pred_loss_5: double
train/reg_loss_1: double
train/reg_loss_2: double
train/reg_loss_3: double
train/reg_loss_4: double
train/reg_loss_5: double
valid/final_loss: double
valid/loss: double
valid/loss_best: double
valid/pred_loss/dataloader_idx_0: double
valid/pred_loss_1/dataloader_idx_0: double
valid/pred_loss_2/dataloader_idx_0: double
valid/pred_loss_3/dataloader_idx_0: double
valid/pred_loss_4/dataloader_idx_0: double
valid/pred_loss_5/dataloader_idx_0: double
valid/reg_loss_1/dataloader_idx_0: double
valid/reg_loss_2/dataloader_idx_0: double
valid/reg_loss_3/dataloader_idx_0: double
valid/reg_loss_4/dataloader_idx_0: double
valid/reg_loss_5/dataloader_idx_0: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 4415
to
{'epoch': Value('float64'), 'lr-Adam': Value('float64'), 'step': Value('int64'), 'train/loss': Value('float64'), 'train/pred_loss': Value('float64'), 'train/pred_loss_1': Value('float64'), 'train/pred_loss_2': Value('float64'), 'train/pred_loss_3': Value('float64'), 'train/reg_loss_1': Value('float64'), 'train/reg_loss_2': Value('float64'), 'train/reg_loss_3': Value('float64'), 'valid/final_loss': Value('float64'), 'valid/loss': Value('float64'), 'valid/loss_best': Value('float64'), 'valid/pred_loss/dataloader_idx_0': Value('float64'), 'valid/pred_loss_1/dataloader_idx_0': Value('float64'), 'valid/pred_loss_2/dataloader_idx_0': Value('float64'), 'valid/pred_loss_3/dataloader_idx_0': Value('float64'), 'valid/reg_loss_1/dataloader_idx_0': Value('float64'), 'valid/reg_loss_2/dataloader_idx_0': Value('float64'), 'valid/reg_loss_3/dataloader_idx_0': 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 1342, 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 907, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, 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 8 new columns ({'valid/reg_loss_5/dataloader_idx_0', 'train/reg_loss_5', 'valid/reg_loss_4/dataloader_idx_0', 'valid/pred_loss_5/dataloader_idx_0', 'valid/pred_loss_4/dataloader_idx_0', 'train/pred_loss_4', 'train/pred_loss_5', 'train/reg_loss_4'})
This happened while the csv dataset builder was generating data using
hf://datasets/MLDS-NUS/Meso-SpecOnsNet/logs/official/runs/ckdv/fno/csv/version_0/metrics.csv (at revision ce28d024519508f1e8145e408caa97f3e5334ded), ['hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/ac/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/ac_realV/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac/s_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d/csv/version_1/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d_realV/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/ac_2d_realV/csv/version_1/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/res_onsagernet_2d/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_2/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_4/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_5/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ac_2d/s_onsagernet/csv/version_6/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/ckdv/s_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/fene_v2/s_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/fno/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/kdv/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/kdv_realV/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/onsagernet1d_lowrank/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/res_onsagernet/csv/version_0/metrics.csv', 'hf://datasets/MLDS-NUS/Meso-SpecOnsNet@ce28d024519508f1e8145e408caa97f3e5334ded/logs/official/runs/kdv/s_onsagernet/csv/version_0/metrics.csv']
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 | lr-Adam float64 | step int64 | train/loss float64 | train/pred_loss float64 | train/pred_loss_1 float64 | train/pred_loss_2 float64 | train/pred_loss_3 float64 | train/reg_loss_1 float64 | train/reg_loss_2 float64 | train/reg_loss_3 float64 | valid/final_loss null | valid/loss null | valid/loss_best null | valid/pred_loss/dataloader_idx_0 null | valid/pred_loss_1/dataloader_idx_0 null | valid/pred_loss_2/dataloader_idx_0 null | valid/pred_loss_3/dataloader_idx_0 null | valid/reg_loss_1/dataloader_idx_0 null | valid/reg_loss_2/dataloader_idx_0 null | valid/reg_loss_3/dataloader_idx_0 null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | 0.001 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
0 | null | 20 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 21 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
1 | null | 41 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 42 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
2 | null | 62 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 63 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
3 | null | 83 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 84 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
4 | null | 104 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 105 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
5 | null | 125 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 126 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
6 | null | 146 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 147 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
7 | null | 167 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 168 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
8 | null | 188 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 189 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
9 | null | 209 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 210 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
10 | null | 230 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 231 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
11 | null | 251 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 252 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
12 | null | 272 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 273 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
13 | null | 293 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 294 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
14 | null | 314 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 315 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
15 | null | 335 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 336 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
16 | null | 356 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 357 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
17 | null | 377 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 378 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
18 | null | 398 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 399 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
19 | null | 419 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 420 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
20 | null | 440 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 441 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
21 | null | 461 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 462 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
22 | null | 482 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 483 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
23 | null | 503 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 504 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
24 | null | 524 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 525 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
25 | null | 545 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 546 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
26 | null | 566 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 567 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
27 | null | 587 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 588 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
28 | null | 608 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 609 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
29 | null | 629 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 630 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
30 | null | 650 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 651 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
31 | null | 671 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 672 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
32 | null | 692 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 693 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
33 | null | 713 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 714 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
34 | null | 734 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 735 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
35 | null | 755 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 756 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
36 | null | 776 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 777 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
37 | null | 797 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 798 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
38 | null | 818 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 819 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
39 | null | 839 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 840 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
40 | null | 860 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 861 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
41 | null | 881 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 882 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
42 | null | 902 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 903 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
43 | null | 923 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 924 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
44 | null | 944 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 945 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
45 | null | 965 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 966 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
46 | null | 986 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 987 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
47 | null | 1,007 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 1,008 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
48 | null | 1,028 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
null | 0.001 | 1,029 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
49 | null | 1,049 | 0.000001 | 0.000002 | 0 | 0.000001 | 0.000002 | 0 | 0 | 0 | null | null | null | null | null | null | null | null | null | null |
End of preview.
Overview
This is the official dataset/checkpoint repository for Meso-SpecOnsNet, including the data generation, network training, and performance visualization for the paper:
Hypothesis-driven construction of mesoscopic dynamics
by Zhuoyuan Li et al.
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