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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 1 new columns ({'Epoch'}) and 2 missing columns ({'magnification', 'data size'}).

This happened while the csv dataset builder was generating data using

hf://datasets/zwang2/breakhis_lora_results/res_everything/train_loss_epoch_100_10%.csv (at revision bbdcc1f6f24c67baf0ca16d08c224f09d8464878)

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
              Epoch: int64
              Ours: double
              ResNet-50: double
              ResNeXt-50: double
              densenet121: double
              AlexNet: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 955
              to
              {'data size': Value(dtype='int64', id=None), 'Ours': Value(dtype='float64', id=None), 'ResNet-50': Value(dtype='float64', id=None), 'ResNeXt-50': Value(dtype='float64', id=None), 'densenet121': Value(dtype='float64', id=None), 'AlexNet': Value(dtype='float64', id=None), 'magnification': 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 1323, 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 938, 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 1 new columns ({'Epoch'}) and 2 missing columns ({'magnification', 'data size'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/zwang2/breakhis_lora_results/res_everything/train_loss_epoch_100_10%.csv (at revision bbdcc1f6f24c67baf0ca16d08c224f09d8464878)
              
              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)

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data size
int64
Ours
float64
ResNet-50
float64
ResNeXt-50
float64
densenet121
float64
AlexNet
float64
magnification
string
5
1
1
1
1
0.821705
high
5
1
1
1
1
1
low
5
1
1
1
1
0.992481
all
5
1
1
1
1
0.895522
40
5
1
1
1
1
1
100
5
1
1
1
1
1
200
5
1
1
1
1
0.967213
400
6
1
1
1
1
1
high
6
1
1
1
1
0.963636
low
6
1
1
1
1
0.99375
all
6
1
1
1
1
0.987654
40
6
1
1
1
1
1
100
6
1
1
1
1
1
200
6
1
1
1
1
0.72973
400
7
1
1
1
1
0.994475
high
7
1
1
1
1
1
low
7
1
1
1
1
0.975871
all
7
1
1
1
1
1
40
7
1
1
1
1
0.979592
100
7
1
1
1
1
1
200
7
1
1
1
1
1
400
8
1
1
1
1
0.941748
high
8
1
1
1
1
1
low
8
1
1
1
1
0.997647
all
8
1
1
1
1
0.953271
40
8
1
1
1
1
1
100
8
1
1
1
1
1
200
8
1
1
1
1
0.928571
400
9
1
1
1
1
0.99569
high
9
1
1
1
1
1
low
9
1
1
1
1
0.993737
all
9
1
1
1
1
0.677686
40
9
1
1
1
1
1
100
9
1
1
1
1
1
200
9
1
1
1
1
1
400
10
1
1
1
1
0.929961
high
10
1
1
1
1
1
low
10
1
1
1
1
0.992467
all
10
1
1
1
1
0.932836
40
10
1
1
1
1
1
100
10
1
1
1
1
1
200
10
1
1
1
1
1
400
11
1
1
1
1
0.957746
high
11
1
1
1
1
1
low
11
1
1
1
1
0.940273
all
11
1
1
1
1
0.993243
40
11
1
1
1
1
0.987013
100
11
1
1
1
1
1
200
11
1
1
1
1
1
400
12
1
1
1
1
1
high
12
1
1
1
1
0.984802
low
12
1
1
1
1
0.990596
all
12
1
1
1
1
0.900621
40
12
1
1
1
1
1
100
12
1
1
1
1
0.981481
200
12
1
1
1
1
0.952381
400
13
1
1
1
1
0.946269
high
13
1
1
1
1
0.985955
low
13
1
1
1
1
0.997106
all
13
1
1
1
1
0.971264
40
13
1
1
1
1
1
100
13
1
1
1
1
1
200
13
1
1
1
1
1
400
14
1
1
1
1
0.955556
high
14
1
1
1
1
0.997396
low
14
1
0.998656
1
1
0.997312
all
14
1
1
1
1
0.994681
40
14
1
1
1
1
0.979592
100
14
1
1
1
1
1
200
14
1
1
1
1
1
400
15
1
1
1
0.997409
0.96114
high
15
1
1
1
1
0.990268
low
15
1
1
1
1
0.993726
all
15
1
1
1
1
0.99005
40
15
1
1
1
1
1
100
15
1
1
1
1
1
200
15
1
1
1
1
0.967213
400
40
1
0.998696
0.998696
0.999027
0.987354
high
40
1
0.998774
0.999085
1
0.994511
low
40
1
0.998586
0.999057
0.999529
0.983027
all
40
1
1
1
1
0.998131
40
40
1
1
1
1
0.998208
100
40
1
1
1
1
0.994444
200
40
1
1
1
1
1
400
70
1
0.999444
1
1
0.991106
high
70
1
1
1
0.999477
0.997386
low
70
1
0.999192
0.999731
0.998922
0.978179
all
70
1
1
1
1
0.996795
40
70
1
1
1
1
0.994882
100
70
1
1
1
1
1
200
70
1
1
1
1
0.985948
400
100
1
0.999611
0.999611
0.999221
0.985208
high
100
1
0.999268
1
0.999268
0.995974
low
100
1
0.999057
0.999057
0.997548
0.934352
all
100
1
0.999252
1
1
0.997756
40
100
1
1
1
1
0.994982
100
100
1
0.999259
1
1
0.996294
200
100
1
0.99918
0.99918
0.99918
0.997541
400
null
0.728711
4.482546
4.662845
6.603554
10.351462
null
null
0.614477
2.32143
2.162864
2.322345
4.892124
null
End of preview.