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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
edition: string
panel_id: string
index: double
raw_index: double
scores: struct<balanced_skill: double, balanced_raw: double, breadth_skill: double>
child 0, balanced_skill: double
child 1, balanced_raw: double
child 2, breadth_skill: double
areas: list<item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, (... 32 chars omitted)
child 0, item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, benchmarks (... 20 chars omitted)
child 0, id: string
child 1, label: string
child 2, raw: double
child 3, skill: double
child 4, coverage: double
child 5, n: int64
child 6, benchmarks: list<item: int64>
child 0, item: int64
benchmarks: struct<1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_inde (... 4909 chars omitted)
child 0, 1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 6, tracks: list<item: null>
child 0, item: null
child 1, 2: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, covera
...
le
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 39, 24: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 40, 26: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 41, 27: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 42, 34: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
coverage: double
note: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
engine: string
counts: struct<ok: int64>
child 0, ok: int64
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'normalized_rank_regret': Value('float64')}, 'positive_micro_f1': Valu
...
Value('int64'), 'mean': Value('int64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
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
edition: string
panel_id: string
index: double
raw_index: double
scores: struct<balanced_skill: double, balanced_raw: double, breadth_skill: double>
child 0, balanced_skill: double
child 1, balanced_raw: double
child 2, breadth_skill: double
areas: list<item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, (... 32 chars omitted)
child 0, item: struct<id: string, label: string, raw: double, skill: double, coverage: double, n: int64, benchmarks (... 20 chars omitted)
child 0, id: string
child 1, label: string
child 2, raw: double
child 3, skill: double
child 4, coverage: double
child 5, n: int64
child 6, benchmarks: list<item: int64>
child 0, item: int64
benchmarks: struct<1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_inde (... 4909 chars omitted)
child 0, 1: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 6, tracks: list<item: null>
child 0, item: null
child 1, 2: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool, t (... 24 chars omitted)
child 0, raw: double
child 1, skill: double
child 2, covera
...
le
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 39, 24: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 40, 26: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 41, 27: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
child 42, 34: struct<raw: double, skill: double, coverage: double, random: double, rule: string, in_index: bool>
child 0, raw: double
child 1, skill: double
child 2, coverage: double
child 3, random: double
child 4, rule: string
child 5, in_index: bool
coverage: double
note: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
engine: string
counts: struct<ok: int64>
child 0, ok: int64
to
{'engine': Value('string'), 'counts': {'ok': Value('int64')}, 'successful_request_latency_ms': {'median': Value('float64'), 'p95': Value('float64'), 'mean': Value('float64')}, 'benchmarks': List({'catalog_id': Value('int64'), 'dataset': Value('string'), 'requests': Value('int64'), 'answered': Value('int64'), 'unsupported': Value('int64'), 'errors': Value('int64'), 'abstained': Value('int64'), 'pending': Value('int64'), 'scored_requests': Value('int64'), 'metric': Value('string'), 'score': Value('float64'), 'reference_same_cases': Value('null'), 'median_ms': Value('float64'), 'detail': {'field_accuracy': Value('float64'), 'case_exact_accuracy': Value('float64'), 'custom_metrics': {'ndcg_at_10': Value('float64'), 'mrr': Value('float64'), 'recall_at_10': Value('float64'), 'candidate_recall': Value('float64'), 'scorable_candidate_recall': Value('float64'), 'candidates_scored': Value('float64'), 'candidates_retrieved': Value('int64'), 'bm25_ndcg_at_10': Value('float64'), 'quality_quality': Value('float64'), 'quality_cost_usd': Value('float64'), 'quality_utility': Value('float64'), 'quality_oracle_optimal': Value('float64'), 'quality_utility_regret': Value('float64'), 'cost_aware_quality': Value('float64'), 'cost_aware_cost_usd': Value('float64'), 'cost_aware_utility': Value('float64'), 'cost_aware_oracle_optimal': Value('float64'), 'cost_aware_utility_regret': Value('float64'), 'value_regret': Value('float64'), 'normalized_rank_regret': Value('float64')}, 'positive_micro_f1': Valu
...
Value('int64'), 'mean': Value('int64')}}, 'cluster_macro_accuracy': Value('float64'), 'positive_f1_by_field': {'sarcastic': Value('float64'), 'sarcasm': Value('float64'), 'irony': Value('float64'), 'satire': Value('float64'), 'understatement': Value('float64'), 'overstatement': Value('float64'), 'rhetorical_question': Value('float64')}, 'category_macro_f1': Value('float64'), 'scored_fields': Value('int64'), 'chance_on_rows': Value('float64')}, 'tracks': {'RouterBench-0shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'RouterBench-5shot': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-A-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-B-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-Ar': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'iSarcasmEval-C-En': {'metric': Value('string'), 'score': Value('null'), 'scored_requests': Value('int64')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}}}), 'note': Value('string'), 'edition': Value('string')}
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.
Darwin-27B-ZTC: Decision Index 0.3 public run
Complete public 0.3 suite run of FINAL-Bench/Darwin-27B-ZTC with the Decision Index kit (commit 9eb2dbe).
runs/darwin-27b-ztc/scores.json: Decision Index 62.63 (edition 0.3, complete: true, 140,178 scored requests, 0 errors, 0 unsupported)runs/darwin-27b-ztc/results.jsonl.gz: all 140,620 result rows, untouched (16 run-id hash shards concatenated in shard order; compact rows)runs/darwin-27b-ztc/shards/: each shard'senvironment.jsonandstatus.json
How it was run. Weights at revision f0ffe63c9f3c697e09c005f74ae6d628eee60d67, served with ztc_server.py from the model repo (POST /v1/systemone; one forward pass per question, no generated tokens; batch 8, token budget 16,000, max length 32,768, refusing longer inputs instead of truncating). The kit's http engine drove 4 server processes on 2× B200 through 16 shards (rows split by sha256 of the run id; each shard runs the kit's own run() with the 0.3 edition filter).
Suite note. The suite was rebuilt locally with suite rebuild --edition 0.3. The rebuilt GSM8K rows and added rows are byte-identical to the pinned hashes; our release-v1 rows are not byte-identical (uncompressed sha256 af4d57cf…), so the v2 cut was run with the kit's own release_v2.main on those rows (uncompressed sha256 aa5e07ff…). Request counts per benchmark match the 0.3 manifest for all 37 static benchmarks. Please re-score the results file against your suite.
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