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noirchoix
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fooddbml_app

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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 6 new columns ({'recall', 'roc_auc', 'f1', 'n_positive', 'average_precision', 'precision'}) and 19 missing columns ({'variant', 'model_path', 'reason', 'test_average_precision', 'val_average_precision', 'split_name', 'n_local_train_unlabeled', 'n_val_pos', 'sample_report', 'test_roc_auc', 'best_iteration', 'n_train_unlabeled_full', 'trained', 'n_test_pos', 'n_local_train_pos', 'n_local_train_rows', 'val_roc_auc', 'head_name', 'n_train_pos_full'}).

This happened while the csv dataset builder was generating data using

hf://datasets/noirchoix/fooddbml_app/artifacts/models/annotation/heads/hard_negative/flavor/scaffold/val_per_label_metrics.csv (at revision da568b5b8bf50984535ad0e4983077468e9bf76a), ['hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/hard_negative/flavor/scaffold/val_per_label_metrics.csv', 'hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/hard_negative/health_effect/scaffold/val_per_label_metrics.csv', 'hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/hard_negative/pathway/scaffold/val_per_label_metrics.csv', 'hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/positive_unlabeled/enzyme/scaffold/val_per_label_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/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              label_idx: int64
              label: int64
              n_positive: int64
              average_precision: double
              roc_auc: double
              f1: double
              precision: double
              recall: double
              threshold: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1323
              to
              {'trained': Value('bool'), 'variant': Value('string'), 'label_idx': Value('float64'), 'label': Value('float64'), 'head_name': Value('string'), 'split_name': Value('string'), 'model_path': Value('string'), 'best_iteration': Value('float64'), 'sample_report': Value('string'), 'n_train_pos_full': Value('float64'), 'n_train_unlabeled_full': Value('float64'), 'n_local_train_rows': Value('float64'), 'n_local_train_pos': Value('float64'), 'n_local_train_unlabeled': Value('float64'), 'n_val_pos': Value('float64'), 'n_test_pos': Value('float64'), 'threshold': Value('float64'), 'val_average_precision': Value('float64'), 'test_average_precision': Value('float64'), 'val_roc_auc': Value('float64'), 'test_roc_auc': Value('float64'), 'reason': Value('string')}
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 6 new columns ({'recall', 'roc_auc', 'f1', 'n_positive', 'average_precision', 'precision'}) and 19 missing columns ({'variant', 'model_path', 'reason', 'test_average_precision', 'val_average_precision', 'split_name', 'n_local_train_unlabeled', 'n_val_pos', 'sample_report', 'test_roc_auc', 'best_iteration', 'n_train_unlabeled_full', 'trained', 'n_test_pos', 'n_local_train_pos', 'n_local_train_rows', 'val_roc_auc', 'head_name', 'n_train_pos_full'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/noirchoix/fooddbml_app/artifacts/models/annotation/heads/hard_negative/flavor/scaffold/val_per_label_metrics.csv (at revision da568b5b8bf50984535ad0e4983077468e9bf76a), ['hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/hard_negative/flavor/scaffold/val_per_label_metrics.csv', 'hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/hard_negative/health_effect/scaffold/val_per_label_metrics.csv', 'hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/hard_negative/pathway/scaffold/val_per_label_metrics.csv', 'hf://datasets/noirchoix/fooddbml_app@da568b5b8bf50984535ad0e4983077468e9bf76a/artifacts/models/annotation/heads/positive_unlabeled/enzyme/scaffold/val_per_label_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.

trained
bool
variant
string
label_idx
float64
label
float64
head_name
string
split_name
string
model_path
string
best_iteration
float64
sample_report
string
n_train_pos_full
float64
n_train_unlabeled_full
float64
n_local_train_rows
float64
n_local_train_pos
float64
n_local_train_unlabeled
float64
n_val_pos
float64
n_test_pos
float64
threshold
float64
val_average_precision
float64
test_average_precision
float64
val_roc_auc
float64
test_roc_auc
float64
reason
null
true
hard_negative
0
44
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0000_44.txt
153
{'variant': 'hard_negative', 'n_positive': 281, 'n_unlabeled_available': 42575, 'n_sampled_hard_negatives': 3372, 'negative_weight': 1.0}
281
42,575
3,653
281
3,372
33
34
0.959764
0.499115
0.446284
0.993847
0.992874
null
true
hard_negative
1
59
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0001_59.txt
55
{'variant': 'hard_negative', 'n_positive': 217, 'n_unlabeled_available': 42639, 'n_sampled_hard_negatives': 2604, 'negative_weight': 1.0}
217
42,639
2,821
217
2,604
28
44
0.864402
0.388531
0.423679
0.993914
0.993634
null
true
hard_negative
2
81
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0002_81.txt
82
{'variant': 'hard_negative', 'n_positive': 211, 'n_unlabeled_available': 42645, 'n_sampled_hard_negatives': 2532, 'negative_weight': 1.0}
211
42,645
2,743
211
2,532
33
44
0.928031
0.487558
0.606361
0.996319
0.996365
null
true
hard_negative
3
123
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0003_123.txt
9
{'variant': 'hard_negative', 'n_positive': 158, 'n_unlabeled_available': 42698, 'n_sampled_hard_negatives': 1896, 'negative_weight': 1.0}
158
42,698
2,054
158
1,896
4
12
0.335395
0.080466
0.089706
0.977694
0.987268
null
true
hard_negative
4
54
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0004_54.txt
22
{'variant': 'hard_negative', 'n_positive': 128, 'n_unlabeled_available': 42728, 'n_sampled_hard_negatives': 1536, 'negative_weight': 1.0}
128
42,728
1,664
128
1,536
13
9
0.567253
0.233031
0.145872
0.987556
0.98157
null
true
hard_negative
5
531
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0005_531.txt
177
{'variant': 'hard_negative', 'n_positive': 123, 'n_unlabeled_available': 42733, 'n_sampled_hard_negatives': 1476, 'negative_weight': 1.0}
123
42,733
1,599
123
1,476
7
12
0.985334
0.185324
0.087404
0.993321
0.99077
null
true
hard_negative
6
33
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0006_33.txt
195
{'variant': 'hard_negative', 'n_positive': 105, 'n_unlabeled_available': 42751, 'n_sampled_hard_negatives': 1260, 'negative_weight': 1.0}
105
42,751
1,365
105
1,260
12
21
0.994984
0.380572
0.157486
0.994275
0.992003
null
true
hard_negative
7
848
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0007_848.txt
152
{'variant': 'hard_negative', 'n_positive': 81, 'n_unlabeled_available': 42775, 'n_sampled_hard_negatives': 972, 'negative_weight': 1.0}
81
42,775
1,053
81
972
18
25
0.992483
0.442944
0.437596
0.995366
0.992323
null
true
hard_negative
8
162
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0008_162.txt
51
{'variant': 'hard_negative', 'n_positive': 81, 'n_unlabeled_available': 42775, 'n_sampled_hard_negatives': 972, 'negative_weight': 1.0}
81
42,775
1,053
81
972
11
11
0.815872
0.160194
0.150366
0.99324
0.992712
null
true
hard_negative
9
18
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0009_18.txt
20
{'variant': 'hard_negative', 'n_positive': 69, 'n_unlabeled_available': 42787, 'n_sampled_hard_negatives': 828, 'negative_weight': 1.0}
69
42,787
897
69
828
17
17
0.55358
0.227198
0.188442
0.990502
0.989076
null
true
hard_negative
10
21
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0010_21.txt
12
{'variant': 'hard_negative', 'n_positive': 78, 'n_unlabeled_available': 42778, 'n_sampled_hard_negatives': 936, 'negative_weight': 1.0}
78
42,778
1,014
78
936
5
5
0.394733
0.030276
0.040176
0.98698
0.986754
null
true
hard_negative
11
50
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0011_50.txt
50
{'variant': 'hard_negative', 'n_positive': 61, 'n_unlabeled_available': 42795, 'n_sampled_hard_negatives': 732, 'negative_weight': 1.0}
61
42,795
793
61
732
10
15
0.844571
0.298985
0.35213
0.99281
0.994635
null
true
hard_negative
12
98
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0012_98.txt
36
{'variant': 'hard_negative', 'n_positive': 71, 'n_unlabeled_available': 42785, 'n_sampled_hard_negatives': 852, 'negative_weight': 1.0}
71
42,785
923
71
852
2
4
0.730436
0.138158
0.042332
0.995807
0.9939
null
true
hard_negative
13
68
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0013_68.txt
59
{'variant': 'hard_negative', 'n_positive': 60, 'n_unlabeled_available': 42796, 'n_sampled_hard_negatives': 720, 'negative_weight': 1.0}
60
42,796
780
60
720
8
3
0.873314
0.101483
0.028736
0.988304
0.990507
null
true
hard_negative
14
34
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0014_34.txt
136
{'variant': 'hard_negative', 'n_positive': 53, 'n_unlabeled_available': 42803, 'n_sampled_hard_negatives': 636, 'negative_weight': 1.0}
53
42,803
689
53
636
6
9
0.947833
0.330542
0.166199
0.998484
0.99512
null
true
hard_negative
15
37
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0015_37.txt
2
{'variant': 'hard_negative', 'n_positive': 58, 'n_unlabeled_available': 42798, 'n_sampled_hard_negatives': 696, 'negative_weight': 1.0}
58
42,798
754
58
696
3
4
0.150072
0.122115
0.106797
0.990832
0.986453
null
true
hard_negative
16
17
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0016_17.txt
10
{'variant': 'hard_negative', 'n_positive': 46, 'n_unlabeled_available': 42810, 'n_sampled_hard_negatives': 552, 'negative_weight': 1.0}
46
42,810
598
46
552
10
7
0.350628
0.089427
0.137234
0.992952
0.994638
null
true
hard_negative
17
164
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0017_164.txt
40
{'variant': 'hard_negative', 'n_positive': 46, 'n_unlabeled_available': 42810, 'n_sampled_hard_negatives': 552, 'negative_weight': 1.0}
46
42,810
598
46
552
7
8
0.782566
0.620876
0.19604
0.997875
0.986597
null
true
hard_negative
18
643
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0018_643.txt
17
{'variant': 'hard_negative', 'n_positive': 41, 'n_unlabeled_available': 42815, 'n_sampled_hard_negatives': 492, 'negative_weight': 1.0}
41
42,815
533
41
492
8
9
0.508804
0.506296
0.102868
0.983004
0.985112
null
true
hard_negative
19
69
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0019_69.txt
41
{'variant': 'hard_negative', 'n_positive': 35, 'n_unlabeled_available': 42821, 'n_sampled_hard_negatives': 420, 'negative_weight': 1.0}
35
42,821
455
35
420
8
14
0.757413
0.418742
0.376811
0.994871
0.99655
null
true
hard_negative
20
229
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0020_229.txt
32
{'variant': 'hard_negative', 'n_positive': 52, 'n_unlabeled_available': 42804, 'n_sampled_hard_negatives': 624, 'negative_weight': 1.0}
52
42,804
676
52
624
1
2
0.643613
0.058824
0.03184
0.998257
0.993521
null
true
hard_negative
21
814
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0021_814.txt
56
{'variant': 'hard_negative', 'n_positive': 36, 'n_unlabeled_available': 42820, 'n_sampled_hard_negatives': 432, 'negative_weight': 1.0}
36
42,820
468
36
432
7
11
0.858102
0.18599
0.04863
0.990122
0.986142
null
true
hard_negative
22
67
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0022_67.txt
226
{'variant': 'hard_negative', 'n_positive': 44, 'n_unlabeled_available': 42812, 'n_sampled_hard_negatives': 528, 'negative_weight': 1.0}
44
42,812
572
44
528
1
8
0.962514
0.076923
0.12277
0.998693
0.989553
null
true
hard_negative
23
213
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0023_213.txt
50
{'variant': 'hard_negative', 'n_positive': 37, 'n_unlabeled_available': 42819, 'n_sampled_hard_negatives': 444, 'negative_weight': 1.0}
37
42,819
481
37
444
5
10
0.836088
0.09792
0.318334
0.9944
0.99533
null
false
hard_negative
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
null
true
hard_negative
25
85
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0025_85.txt
35
{'variant': 'hard_negative', 'n_positive': 38, 'n_unlabeled_available': 42818, 'n_sampled_hard_negatives': 456, 'negative_weight': 1.0}
38
42,818
494
38
456
7
6
0.737208
0.708576
0.500878
0.999377
0.998611
null
true
hard_negative
26
95
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0026_95.txt
74
{'variant': 'hard_negative', 'n_positive': 40, 'n_unlabeled_available': 42816, 'n_sampled_hard_negatives': 480, 'negative_weight': 1.0}
40
42,816
520
40
480
4
6
0.933838
0.344375
0.105748
0.995002
0.996178
null
true
hard_negative
27
200
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0027_200.txt
66
{'variant': 'hard_negative', 'n_positive': 39, 'n_unlabeled_available': 42817, 'n_sampled_hard_negatives': 468, 'negative_weight': 1.0}
39
42,817
507
39
468
1
8
0.872299
0.032258
0.384138
0.996733
0.997119
null
true
hard_negative
28
711
flavor
scaffold
/kaggle/working/artifacts/fooddb_compound_functional_annotation_predictor_v1_3_hybrid_pu_hardneg/models/hard_negative/flavor/scaffold/label_0028_711.txt
100
{'variant': 'hard_negative', 'n_positive': 42, 'n_unlabeled_available': 42814, 'n_sampled_hard_negatives': 504, 'negative_weight': 1.0}
42
42,814
546
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23
{'variant': 'hard_negative', 'n_positive': 160, 'n_unlabeled_available': 42696, 'n_sampled_hard_negatives': 1920, 'negative_weight': 1.0}
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{'variant': 'hard_negative', 'n_positive': 134, 'n_unlabeled_available': 42722, 'n_sampled_hard_negatives': 1608, 'negative_weight': 1.0}
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{'variant': 'hard_negative', 'n_positive': 156, 'n_unlabeled_available': 42700, 'n_sampled_hard_negatives': 1872, 'negative_weight': 1.0}
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50
{'variant': 'hard_negative', 'n_positive': 102, 'n_unlabeled_available': 42754, 'n_sampled_hard_negatives': 1224, 'negative_weight': 1.0}
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34
{'variant': 'hard_negative', 'n_positive': 105, 'n_unlabeled_available': 42751, 'n_sampled_hard_negatives': 1260, 'negative_weight': 1.0}
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{'variant': 'hard_negative', 'n_positive': 103, 'n_unlabeled_available': 42753, 'n_sampled_hard_negatives': 1236, 'negative_weight': 1.0}
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42
{'variant': 'hard_negative', 'n_positive': 96, 'n_unlabeled_available': 42760, 'n_sampled_hard_negatives': 1152, 'negative_weight': 1.0}
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{'variant': 'hard_negative', 'n_positive': 89, 'n_unlabeled_available': 42767, 'n_sampled_hard_negatives': 1068, 'negative_weight': 1.0}
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57
{'variant': 'hard_negative', 'n_positive': 82, 'n_unlabeled_available': 42774, 'n_sampled_hard_negatives': 984, 'negative_weight': 1.0}
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{'variant': 'hard_negative', 'n_positive': 68, 'n_unlabeled_available': 42788, 'n_sampled_hard_negatives': 816, 'negative_weight': 1.0}
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7
{'variant': 'hard_negative', 'n_positive': 68, 'n_unlabeled_available': 42788, 'n_sampled_hard_negatives': 816, 'negative_weight': 1.0}
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178
{'variant': 'hard_negative', 'n_positive': 61, 'n_unlabeled_available': 42795, 'n_sampled_hard_negatives': 732, 'negative_weight': 1.0}
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139
{'variant': 'hard_negative', 'n_positive': 61, 'n_unlabeled_available': 42795, 'n_sampled_hard_negatives': 732, 'negative_weight': 1.0}
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32
{'variant': 'hard_negative', 'n_positive': 64, 'n_unlabeled_available': 42792, 'n_sampled_hard_negatives': 768, 'negative_weight': 1.0}
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{'variant': 'hard_negative', 'n_positive': 58, 'n_unlabeled_available': 42798, 'n_sampled_hard_negatives': 696, 'negative_weight': 1.0}
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scaffold
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101
{'variant': 'hard_negative', 'n_positive': 60, 'n_unlabeled_available': 42796, 'n_sampled_hard_negatives': 720, 'negative_weight': 1.0}
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null
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