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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
scenario: string
strategy: string
run_id: string
engine_version: string
hour: int64
solar_kw: double
demand_kw: double
served_kw: double
unserved_kw: double
battery_kw: double
soc_pct: double
curtailed_kw: int64
sectors: struct<health: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string (... 485 chars omitted)
child 0, health: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 1, water: struct<demand_kw: int64, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: int64
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 2, school: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 3, households: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 4, market: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 5, industry: struct<demand_kw: double, served_kw: double, critical_served_kw: int64, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: int64
child 3, mode: string
id: string
prompt: string
expectTool: string
expectEvidence: bool
to
{'id': Value('string'), 'prompt': Value('string'), 'expectTool': Value('string'), 'expectEvidence': Value('bool')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 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
scenario: string
strategy: string
run_id: string
engine_version: string
hour: int64
solar_kw: double
demand_kw: double
served_kw: double
unserved_kw: double
battery_kw: double
soc_pct: double
curtailed_kw: int64
sectors: struct<health: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string (... 485 chars omitted)
child 0, health: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 1, water: struct<demand_kw: int64, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: int64
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 2, school: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 3, households: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 4, market: struct<demand_kw: double, served_kw: double, critical_served_kw: double, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: double
child 3, mode: string
child 5, industry: struct<demand_kw: double, served_kw: double, critical_served_kw: int64, mode: string>
child 0, demand_kw: double
child 1, served_kw: double
child 2, critical_served_kw: int64
child 3, mode: string
id: string
prompt: string
expectTool: string
expectEvidence: bool
to
{'id': Value('string'), 'prompt': Value('string'), 'expectTool': Value('string'), 'expectEvidence': Value('bool')}
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.
Mitsinjo Grid — dispatch benchmark
SIMULATED DATA — every number in this dataset is produced by a deterministic TypeScript energy model of a fictional Malagasy village microgrid. No real meters, no real people, no personal data.
Hourly dispatch results for 8 scenarios × 3 load-shedding strategies
(proportional, essential_first, balanced), plus the synthetic copilot evaluation prompts.
Source: Tokiarivelo/mitsinjo-grid —
reproduce with make bench && make gen-evals && make hf-dataset.
Files
| File | Rows | Content |
|---|---|---|
scenarios.jsonl |
8 | One scenario input per line: scenario, title, description, input (24 h horizon, 1 h steps, 6 sectors, 120 kWh battery unless the scenario says otherwise) |
results.jsonl |
576 | One hourly step per line: scenario, strategy, run_id (engine SIM-… id), engine_version, hour (0–23), kW flows (solar_kw, demand_kw, served_kw, unserved_kw, battery_kw signed, curtailed_kw), soc_pct, and per-sector demand_kw / served_kw / critical_served_kw / mode |
copilot_evals.jsonl |
12 | Synthetic copilot prompts: id, prompt, expectTool (engine tool the copilot should call), expectEvidence (whether the answer must cite computed numbers) |
Day-level metrics (coverage %, unserved kWh) are derived by summing the hourly rows; see
benchmarks/ for the
reference strategy A/B report.
License
CC-BY-4.0. If you use this dataset, please link back to https://huggingface.co/datasets/Tokiarivelo/mitsinjo-grid-dispatch-benchmark.
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