Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
name: string
kind: string
input_kind: string
best: struct<epoch: int64, lr: double, train_loss: double, val_loss: double, train_accuracy: double, train (... 680 chars omitted)
child 0, epoch: int64
child 1, lr: double
child 2, train_loss: double
child 3, val_loss: double
child 4, train_accuracy: double
child 5, train_macro_f1: double
child 6, train_macro_precision: double
child 7, train_macro_recall: double
child 8, train_balanced_accuracy: double
child 9, train_f1_nuclear_explosion: double
child 10, train_f1_earthquake: double
child 11, train_f1_deep_earthquake: double
child 12, train_f1_explosion: double
child 13, train_f1_noise: double
child 14, train_f1_volcanic_eruption: double
child 15, train_recall_nuclear_explosion: double
child 16, val_accuracy: double
child 17, val_macro_f1: double
child 18, val_macro_precision: double
child 19, val_macro_recall: double
child 20, val_balanced_accuracy: double
child 21, val_f1_nuclear_explosion: double
child 22, val_f1_earthquake: double
child 23, val_f1_deep_earthquake: double
child 24, val_f1_explosion: double
child 25, val_f1_noise: double
child 26, val_f1_volcanic_eruption: double
child 27, val_recall_nuclear_explosion: double
to
{'best': {'epoch': Value('int64'), 'lr': Value('float64'), 'train_loss': Value('float64'), 'val_loss': Value('float64'), 'train_accuracy': Value('float64'), 'train_macro_f1': Value('float64'), 'train_macro_precision': Value('float64'), 'train_macro_recall': Value('float64'), 'train_balanced_accuracy': Value('float64'), 'train_f1_nuclear_explosion': Value('float64'), 'train_f1_earthquake': Value('float64'), 'train_f1_deep_earthquake': Value('float64'), 'train_f1_explosion': Value('float64'), 'train_f1_noise': Value('float64'), 'train_f1_volcanic_eruption': Value('float64'), 'train_recall_nuclear_explosion': Value('float64'), 'val_accuracy': Value('float64'), 'val_macro_f1': Value('float64'), 'val_macro_precision': Value('float64'), 'val_macro_recall': Value('float64'), 'val_balanced_accuracy': Value('float64'), 'val_f1_nuclear_explosion': Value('float64'), 'val_f1_earthquake': Value('float64'), 'val_f1_deep_earthquake': Value('float64'), 'val_f1_explosion': Value('float64'), 'val_f1_noise': Value('float64'), 'val_f1_volcanic_eruption': Value('float64'), 'val_recall_nuclear_explosion': Value('float64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_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
name: string
kind: string
input_kind: string
best: struct<epoch: int64, lr: double, train_loss: double, val_loss: double, train_accuracy: double, train (... 680 chars omitted)
child 0, epoch: int64
child 1, lr: double
child 2, train_loss: double
child 3, val_loss: double
child 4, train_accuracy: double
child 5, train_macro_f1: double
child 6, train_macro_precision: double
child 7, train_macro_recall: double
child 8, train_balanced_accuracy: double
child 9, train_f1_nuclear_explosion: double
child 10, train_f1_earthquake: double
child 11, train_f1_deep_earthquake: double
child 12, train_f1_explosion: double
child 13, train_f1_noise: double
child 14, train_f1_volcanic_eruption: double
child 15, train_recall_nuclear_explosion: double
child 16, val_accuracy: double
child 17, val_macro_f1: double
child 18, val_macro_precision: double
child 19, val_macro_recall: double
child 20, val_balanced_accuracy: double
child 21, val_f1_nuclear_explosion: double
child 22, val_f1_earthquake: double
child 23, val_f1_deep_earthquake: double
child 24, val_f1_explosion: double
child 25, val_f1_noise: double
child 26, val_f1_volcanic_eruption: double
child 27, val_recall_nuclear_explosion: double
to
{'best': {'epoch': Value('int64'), 'lr': Value('float64'), 'train_loss': Value('float64'), 'val_loss': Value('float64'), 'train_accuracy': Value('float64'), 'train_macro_f1': Value('float64'), 'train_macro_precision': Value('float64'), 'train_macro_recall': Value('float64'), 'train_balanced_accuracy': Value('float64'), 'train_f1_nuclear_explosion': Value('float64'), 'train_f1_earthquake': Value('float64'), 'train_f1_deep_earthquake': Value('float64'), 'train_f1_explosion': Value('float64'), 'train_f1_noise': Value('float64'), 'train_f1_volcanic_eruption': Value('float64'), 'train_recall_nuclear_explosion': Value('float64'), 'val_accuracy': Value('float64'), 'val_macro_f1': Value('float64'), 'val_macro_precision': Value('float64'), 'val_macro_recall': Value('float64'), 'val_balanced_accuracy': Value('float64'), 'val_f1_nuclear_explosion': Value('float64'), 'val_f1_earthquake': Value('float64'), 'val_f1_deep_earthquake': Value('float64'), 'val_f1_explosion': Value('float64'), 'val_f1_noise': Value('float64'), 'val_f1_volcanic_eruption': Value('float64'), 'val_recall_nuclear_explosion': Value('float64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Seismic Ensemble — Data & Model Store
This repo holds the data and model artefacts for the code at irp-jas25, a project on multi-class seismic event discrimination (earthquake / deep earthquake / explosion / nuclear explosion / volcanic eruption / noise) for CTBT-style monitoring, using a six-member ensemble (four deep models, two classical) with a stacked meta-learner on top. It accompanies the paper "Robust and Explainable Multi-Class Seismic Event Discrimination through Benchmarking and Ensemble Learning."
Start here: CANONICAL/tune5/
CANONICAL/tune5/ is the definitive run behind the final report — everything you need to reproduce the
paper's tables and figures lives here, and nothing outside it is required to do so.
- Model checkpoints:
CANONICAL/tune5/{barama,kasburg,swaine,kong,li,pignatelli}/best_retrain/— one directory per ensemble member, each with its trained weights (model.ptfor the three deep models,model.joblibfor the three classical ones), the config it was trained with, and its Optuna tuning history under the siblingoptuna/folder. In the paper these six go by codenames:barama= CNN,kasburg= ViT,swaine= SWP-Net,kong= Phys-CNN,li= XGBoost,pignatelli= SVM. - Main results:
CANONICAL/tune5/ensemble_hand_plus_ssl_temperature/andCANONICAL/tune5/ensemble_hand_plus_sslare Temperatuer and Platt - calibrated model outcomes respectively.
- Everything else in this folder (
exp_vol_*,exp_spatial_*,exp_hparam_*,exp_pick_perturbation,ensemble_depth_*,anomaly_*,screening_*,pretrain_mae*,tune5_fewshot_*,north_korea_padding_attribution,meta_learner_shap_comparison) backs one specific robustness check, ablation, or appendix figure in the paper — these are the raw outputs behind those sections, not alternative models to pick between. CANONICAL/tune5/results/splits/holds the exact train/val/test split used everywhere above, so any of it can be reproduced end to end.
Everything else in the repo
checkpoints/, experiments/, processed/, and UNIFIED/ are kept for completeness and traceability —
they're the trail of earlier iterations (tune4_colab, ssl1_colab, exp1_colab, and so on) that either
fed into what became tune5 or were tried and set aside along the way. UNIFIED/ processed/ are the
dataset at two stages of the pipeline (unified archives and full processing applied)s.
If you're only after the results and checkpoints that the paper actually reports, you want
CANONICAL/tune5/ above.
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