csoai/meok-process-world-partial-v1
Tabular Regression • Updated
Dataset Preview
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
candidate_count: int64
candidate_validation: list<item: struct<epochs: int64, fit_seconds: double, name: string, validation_mae: double>>
child 0, item: struct<epochs: int64, fit_seconds: double, name: string, validation_mae: double>
child 0, epochs: int64
child 1, fit_seconds: double
child 2, name: string
child 3, validation_mae: double
cosmos_weights_changed: bool
episode_count: int64
fresh_episode_range: list<item: int64>
child 0, item: int64
gspc_admitted: bool
metrics: struct<partial_best: struct<selected: string, test: struct<mae: double, max_abs_error: double, out_o (... 8535 chars omitted)
child 0, partial_best: struct<selected: string, test: struct<mae: double, max_abs_error: double, out_of_range_fraction: dou (... 372 chars omitted)
child 0, selected: string
child 1, test: struct<mae: double, max_abs_error: double, out_of_range_fraction: double, p95_abs_error: double, row (... 37 chars omitted)
child 0, mae: double
child 1, max_abs_error: double
child 2, out_of_range_fraction: double
child 3, p95_abs_error: double
child 4, rows: int64
child 5, sum_constraint_mae: double
child 2, test_hybrid: struct<mae: double, max_abs_error: double, out_of_range_fraction: double, p95_abs_error: double, row (... 37 chars omitted)
child 0, mae: double
child 1, max_abs_error: double
child 2, out_of_range_fraction: double
child 3, p95_abs_e
...
kages/world-rehearsal/rehearsal/dependencies.py: string
child 5, packages/world-rehearsal/rehearsal/engine.py: string
child 6, packages/world-rehearsal/third_party/earth_scale/tempo.py: string
child 7, packages/world-rehearsal/third_party/swarmchat/__init__.py: string
source_revision: string
specialists_validation: struct<0.0: struct<epochs: int64, name: string, validation_mae: double>, 1.0: struct<epochs: int64, (... 38 chars omitted)
child 0, 0.0: struct<epochs: int64, name: string, validation_mae: double>
child 0, epochs: int64
child 1, name: string
child 2, validation_mae: double
child 1, 1.0: struct<epochs: int64, name: string, validation_mae: double>
child 0, epochs: int64
child 1, name: string
child 2, validation_mae: double
split_episode_ranges: struct<test: list<item: int64>, train: list<item: int64>, validation: list<item: int64>>
child 0, test: list<item: int64>
child 0, item: int64
child 1, train: list<item: int64>
child 0, item: int64
child 2, validation: list<item: int64>
child 0, item: int64
split_rows: struct<test: int64, train: int64, validation: int64>
child 0, test: int64
child 1, train: int64
child 2, validation: int64
status: string
test_policy: string
v0_privileged_weights_sha256: string
weights_sha256: string
source_scenario_id: string
features: list<item: double>
child 0, item: double
split: string
episode: int64
target: list<item: double>
child 0, item: double
tick: int64
to
{'episode': Value('int64'), 'features': List(Value('float64')), 'source_scenario_id': Value('string'), 'split': Value('string'), 'target': List(Value('float64')), 'tick': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
candidate_count: int64
candidate_validation: list<item: struct<epochs: int64, fit_seconds: double, name: string, validation_mae: double>>
child 0, item: struct<epochs: int64, fit_seconds: double, name: string, validation_mae: double>
child 0, epochs: int64
child 1, fit_seconds: double
child 2, name: string
child 3, validation_mae: double
cosmos_weights_changed: bool
episode_count: int64
fresh_episode_range: list<item: int64>
child 0, item: int64
gspc_admitted: bool
metrics: struct<partial_best: struct<selected: string, test: struct<mae: double, max_abs_error: double, out_o (... 8535 chars omitted)
child 0, partial_best: struct<selected: string, test: struct<mae: double, max_abs_error: double, out_of_range_fraction: dou (... 372 chars omitted)
child 0, selected: string
child 1, test: struct<mae: double, max_abs_error: double, out_of_range_fraction: double, p95_abs_error: double, row (... 37 chars omitted)
child 0, mae: double
child 1, max_abs_error: double
child 2, out_of_range_fraction: double
child 3, p95_abs_error: double
child 4, rows: int64
child 5, sum_constraint_mae: double
child 2, test_hybrid: struct<mae: double, max_abs_error: double, out_of_range_fraction: double, p95_abs_error: double, row (... 37 chars omitted)
child 0, mae: double
child 1, max_abs_error: double
child 2, out_of_range_fraction: double
child 3, p95_abs_e
...
kages/world-rehearsal/rehearsal/dependencies.py: string
child 5, packages/world-rehearsal/rehearsal/engine.py: string
child 6, packages/world-rehearsal/third_party/earth_scale/tempo.py: string
child 7, packages/world-rehearsal/third_party/swarmchat/__init__.py: string
source_revision: string
specialists_validation: struct<0.0: struct<epochs: int64, name: string, validation_mae: double>, 1.0: struct<epochs: int64, (... 38 chars omitted)
child 0, 0.0: struct<epochs: int64, name: string, validation_mae: double>
child 0, epochs: int64
child 1, name: string
child 2, validation_mae: double
child 1, 1.0: struct<epochs: int64, name: string, validation_mae: double>
child 0, epochs: int64
child 1, name: string
child 2, validation_mae: double
split_episode_ranges: struct<test: list<item: int64>, train: list<item: int64>, validation: list<item: int64>>
child 0, test: list<item: int64>
child 0, item: int64
child 1, train: list<item: int64>
child 0, item: int64
child 2, validation: list<item: int64>
child 0, item: int64
split_rows: struct<test: int64, train: int64, validation: int64>
child 0, test: int64
child 1, train: int64
child 2, validation: int64
status: string
test_policy: string
v0_privileged_weights_sha256: string
weights_sha256: string
source_scenario_id: string
features: list<item: double>
child 0, item: double
split: string
episode: int64
target: list<item: double>
child 0, item: double
tick: int64
to
{'episode': Value('int64'), 'features': List(Value('float64')), 'source_scenario_id': Value('string'), 'split': Value('string'), 'target': List(Value('float64')), 'tick': Value('int64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, 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 956, 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 1702, 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 1887, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
episode int64 | features list | source_scenario_id string | split string | target list | tick int64 |
|---|---|---|---|---|---|
1,000 | [
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0,
0.5,
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] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
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0.3333333333333333,
0
] | 0 |
1,000 | [
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0.3333333333333333,
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1,000 | [
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] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0
] | 2 |
1,000 | [
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0.3333333333333333,
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0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
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0.125,
0,
0.375
] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0
] | 3 |
1,000 | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
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0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
0,
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0.5
] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0
] | 4 |
1,000 | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
1,
-0.125,
0,
0.625
] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0
] | 5 |
1,000 | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
1,
-0.25,
0,
0.75
] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0
] | 6 |
1,000 | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0,
1,
-0.375,
0,
0.875
] | sha256:37eddd9bfca6a0f4882a895b5f73954162b4054039043e846135cadfd8e9b956 | train | [
0.3333333333333333,
0.3333333333333333,
0.3333333333333333,
0
] | 7 |
1,001 | [
0,
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0,
1,
0,
0,
0,
1,
0,
1.125,
1,
0
] | sha256:2f59909de38e2993245e0ae42bfc3eda4147d4744d458733000b11ba0d7a3699 | train | [
0.4,
0.4,
0.2,
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] | 0 |
1,001 | [
0.4,
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0.2,
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0,
0,
0,
1,
0,
1,
1,
0.125
] | sha256:2f59909de38e2993245e0ae42bfc3eda4147d4744d458733000b11ba0d7a3699 | train | [
0.4,
0.4,
0.2,
0
] | 1 |
1,001 | [
0.4,
0.4,
0.2,
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0.4,
0.4,
0.2,
0,
0,
0.875,
1,
0.25
] | sha256:2f59909de38e2993245e0ae42bfc3eda4147d4744d458733000b11ba0d7a3699 | train | [
0.4,
0.4,
0.2,
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] | 2 |
1,001 | [
0.4,
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0.2,
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0.4,
0.4,
0.2,
0,
0,
0.75,
1,
0.375
] | sha256:2f59909de38e2993245e0ae42bfc3eda4147d4744d458733000b11ba0d7a3699 | train | [
0.4,
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] | 3 |
1,001 | [
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0.4,
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0.625,
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] | sha256:2f59909de38e2993245e0ae42bfc3eda4147d4744d458733000b11ba0d7a3699 | train | [
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1,001 | [
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1,001 | [
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1,001 | [
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1,002 | [
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] | 0 |
1,002 | [
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] | sha256:b04eec6521705bfdbf9b313c6b58908e64bcf8a6d23c105ed949a5a4f069befb | train | [
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] | 1 |
1,002 | [
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] | sha256:b04eec6521705bfdbf9b313c6b58908e64bcf8a6d23c105ed949a5a4f069befb | train | [
0.30303030303030304,
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1,002 | [
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] | sha256:b04eec6521705bfdbf9b313c6b58908e64bcf8a6d23c105ed949a5a4f069befb | train | [
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] | 3 |
1,002 | [
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] | sha256:b04eec6521705bfdbf9b313c6b58908e64bcf8a6d23c105ed949a5a4f069befb | train | [
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] | 4 |
1,002 | [
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] | sha256:b04eec6521705bfdbf9b313c6b58908e64bcf8a6d23c105ed949a5a4f069befb | train | [
0.30303030303030304,
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] | 5 |
1,002 | [
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-0.625,
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] | 6 |
1,002 | [
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0,
0.30303030303030304,
0.21212121212121213,
0.48484848484848486,
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1,
-0.75,
0,
0.875
] | sha256:b04eec6521705bfdbf9b313c6b58908e64bcf8a6d23c105ed949a5a4f069befb | train | [
0.30303030303030304,
0.21212121212121213,
0.48484848484848486,
0
] | 7 |
1,003 | [
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1,
0,
1.125,
1,
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] | sha256:f1f59d75d439f7dc2abd3558c050cc378c9bf9027365dddc1ae50bf65697ed16 | train | [
0.36507936507936506,
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0.31746031746031744,
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] | 0 |
1,003 | [
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0.31746031746031744,
0,
0,
0,
0,
1,
0,
1,
1,
0.125
] | sha256:f1f59d75d439f7dc2abd3558c050cc378c9bf9027365dddc1ae50bf65697ed16 | train | [
0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
0
] | 1 |
1,003 | [
0.36507936507936506,
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0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
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0.875,
1,
0.25
] | sha256:f1f59d75d439f7dc2abd3558c050cc378c9bf9027365dddc1ae50bf65697ed16 | train | [
0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
0
] | 2 |
1,003 | [
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0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
0,
0,
0.75,
1,
0.375
] | sha256:f1f59d75d439f7dc2abd3558c050cc378c9bf9027365dddc1ae50bf65697ed16 | train | [
0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
0
] | 3 |
1,003 | [
0.36507936507936506,
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0.31746031746031744,
0,
0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
0,
0,
0.625,
1,
0.5
] | sha256:f1f59d75d439f7dc2abd3558c050cc378c9bf9027365dddc1ae50bf65697ed16 | train | [
0.36507936507936506,
0.31746031746031744,
0.31746031746031744,
0
] | 4 |
1,003 | [
0.36507936507936506,
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End of preview.
MEOK partial-observation process transitions v1
Fresh synthetic transitions from the Council's pinned procurement rehearsal engine.
Splits
- train.jsonl: 1,600 rows from episode IDs 1000–1199 (200 episodes)
- validation.jsonl: 400 rows from IDs 1200–1249 (50 episodes)
- test.jsonl: 560 rows from IDs 1250–1319 (70 episodes)
- test-predictions.jsonl: held-out target and baseline/model predictions
The split is episode-disjoint. Each public row contains 12 partial-observation features and a four-component next-state target. The full simulator profile proportions are excluded from the public dataset. They are used internally only to evaluate the unchanged v0 privileged-input comparator; no privileged feature values are published.
Provenance
The generator pins eight source files from Council commit 4070aa6d3140ba10226677840f4873fbedd2467e. Exact source hashes and training metrics are in SOURCE.json and training-report.json.
Limitations
This is synthetic procurement-process data. It does not represent real people, customers, markets, institutions or physical dynamics. It is not a general world model, and model predictions do not authorise actions.
Interactive browser demo: https://csoai-meok-oowm-hybrid-lab.static.hf.space/partial-observation-v1/index.html
Model: https://huggingface.co/csoai/meok-process-world-partial-v1
Source and CI: https://github.com/CSOAI-ORG/councilof-ai/pull/4285
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Models trained or fine-tuned on csoai/meok-process-world-partial-transitions-v1