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
engine: string
engine_options: struct<base_url: string, model: string>
child 0, base_url: string
child 1, model: string
model_source: struct<kind: string, base_url: string, model: string, request_options: struct<>, policy: string>
child 0, kind: string
child 1, base_url: string
child 2, model: string
child 3, request_options: struct<>
child 4, policy: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
child 0, item: string
runner_sha256: string
latency: string
shards: struct<count: int64, rule: string, note: string, environments: list<item: struct<engine: string, eng (... 292 chars omitted)
child 0, count: int64
child 1, rule: string
child 2, note: string
child 3, environments: list<item: struct<engine: string, engine_options: struct<base_url: string, model: string>, model_sou (... 228 chars omitted)
child 0, item: struct<engine: string, engine_options: struct<base_url: string, model: string>, model_source: struct (... 216 chars omitted)
child 0, engine: string
child 1, engine_options: struct<base_url: string, model: string>
child 0, base_url: string
child 1, model: string
child 2, model_source: struct<kind: string, base_url: string, model: string, request_options: struct<>, policy: string>
child 0, kind: string
child 1, base_url: string
child 2, model: string
child 3, request_options: struct<>
...
struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 3, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 4, iSarcasmEval-A-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 5, iSarcasmEval-A-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 6, iSarcasmEval-B-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, iSarcasmEval-C-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 8, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
edition: string
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'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': V
...
lue('int64'), 'mean': Value('float64')}}, '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')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'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')}}}), '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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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
engine: string
engine_options: struct<base_url: string, model: string>
child 0, base_url: string
child 1, model: string
model_source: struct<kind: string, base_url: string, model: string, request_options: struct<>, policy: string>
child 0, kind: string
child 1, base_url: string
child 2, model: string
child 3, request_options: struct<>
child 4, policy: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
child 0, item: string
runner_sha256: string
latency: string
shards: struct<count: int64, rule: string, note: string, environments: list<item: struct<engine: string, eng (... 292 chars omitted)
child 0, count: int64
child 1, rule: string
child 2, note: string
child 3, environments: list<item: struct<engine: string, engine_options: struct<base_url: string, model: string>, model_sou (... 228 chars omitted)
child 0, item: struct<engine: string, engine_options: struct<base_url: string, model: string>, model_source: struct (... 216 chars omitted)
child 0, engine: string
child 1, engine_options: struct<base_url: string, model: string>
child 0, base_url: string
child 1, model: string
child 2, model_source: struct<kind: string, base_url: string, model: string, request_options: struct<>, policy: string>
child 0, kind: string
child 1, base_url: string
child 2, model: string
child 3, request_options: struct<>
...
struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 3, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 4, iSarcasmEval-A-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 5, iSarcasmEval-A-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 6, iSarcasmEval-B-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, iSarcasmEval-C-Ar: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 8, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
edition: string
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'), 'brier': Value('float64'), 'log_loss': Value('float64'), 'accuracy': V
...
lue('int64'), 'mean': Value('float64')}}, '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')}, 'GSM8K-10choice': {'metric': Value('string'), 'score': Value('float64'), 'scored_requests': Value('int64')}, 'GSM8K-4choice': {'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')}}}), '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.
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RYOTIDE-Qwen v0.3.1 — Decision Index 0.2.1 run
Complete run of the Decision Index edition 0.2.1
(kit commit 87d4650) against the RYOTIDE server v0.3.1, preset ryotide-qwen
(Qwen/Qwen3.5-4B @ 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a, no fine-tuning; prompt hash 0f4adc92116f),
engine http (POST /v1/systemone).
- Decision Index (balanced skill): 36.63; balanced raw 52.06; breadth skill 34.74
complete: true— 150,317 / 150,317 scored requestsok, 0 unsupported, 0 errors- Hardware: 1× NVIDIA RTX PRO 6000 Blackwell Server Edition (96 GB), RunPod
Request text removed, hashes kept
The suite is not redistributable, so results.jsonl.gz omits the payload field (the request
text) and raw_output (identical to response in every row), as the kit's --compact mode does.
Every row keeps the suite's own payload_sha256. Before removing the text we checked that, for all
150,759 rows, sha256(dumps({"model": "jev-1.13.0", "state": ..., "questions": ...})) of the request
we actually sent equals that payload_sha256 — i.e. every request was the unmodified suite row.
Scoring does not read either removed field, so the file re-scores as is:
python -m decision_index score --results results.jsonl # after gunzip, with the suite built
The unredacted file (843 MB) is kept and available to the maintainers on request.
Sharding
The server answers one request at a time, so the run used several server processes on the same
GPU: request r belongs to shard crc32(r.run_id) % N. It started as 3 shards and was re-split into
5 for the remaining rows; the merged file has exactly one row per run_id. environment.json
records every shard's environment under shards. Latencies in scores.json were measured under
that contention and are not the board's single-process figure.
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