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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
parts: struct<jev-style-0.8b-decision-v3: struct<engine: string, engine_options: struct<model: string, revi (... 1467 chars omitted)
child 0, jev-style-0.8b-decision-v3: struct<engine: string, engine_options: struct<model: string, revision: string>, model_source: struct (... 654 chars omitted)
child 0, engine: string
child 1, engine_options: struct<model: string, revision: string>
child 0, model: string
child 1, revision: string
child 2, model_source: struct<model: string, hf_repo: string, revision: string, snapshot: string, runtime_sha256: string, m (... 282 chars omitted)
child 0, model: string
child 1, hf_repo: string
child 2, revision: string
child 3, snapshot: string
child 4, runtime_sha256: string
child 5, manifest_verified: struct<checked: int64, bad: list<item: null>, missing_ignored: list<item: null>>
child 0, checked: int64
child 1, bad: list<item: null>
child 0, item: null
child 2, missing_ignored: list<item: null>
child 0, item: null
child 6, device: string
child 7, dtype: string
child 8, template: string
child 9, context_limit: int64
child 10, head_max: int64
child 11, temperature: string
child 12, no_truncation: bool
child 13, option_filtering: bool
child 14, per_benchmark_prompts: bool
child
...
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
note: string
edition: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
counts: struct<ok: int64, unsupported: int64>
child 0, ok: int64
child 1, unsupported: int64
engine: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64'), 'unsupported': 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('int64'), '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': Va
...
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
parts: struct<jev-style-0.8b-decision-v3: struct<engine: string, engine_options: struct<model: string, revi (... 1467 chars omitted)
child 0, jev-style-0.8b-decision-v3: struct<engine: string, engine_options: struct<model: string, revision: string>, model_source: struct (... 654 chars omitted)
child 0, engine: string
child 1, engine_options: struct<model: string, revision: string>
child 0, model: string
child 1, revision: string
child 2, model_source: struct<model: string, hf_repo: string, revision: string, snapshot: string, runtime_sha256: string, m (... 282 chars omitted)
child 0, model: string
child 1, hf_repo: string
child 2, revision: string
child 3, snapshot: string
child 4, runtime_sha256: string
child 5, manifest_verified: struct<checked: int64, bad: list<item: null>, missing_ignored: list<item: null>>
child 0, checked: int64
child 1, bad: list<item: null>
child 0, item: null
child 2, missing_ignored: list<item: null>
child 0, item: null
child 6, device: string
child 7, dtype: string
child 8, template: string
child 9, context_limit: int64
child 10, head_max: int64
child 11, temperature: string
child 12, no_truncation: bool
child 13, option_filtering: bool
child 14, per_benchmark_prompts: bool
child
...
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
note: string
edition: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
counts: struct<ok: int64, unsupported: int64>
child 0, ok: int64
child 1, unsupported: int64
engine: string
to
{'engine': Value('string'), 'counts': {'ok': Value('int64'), 'unsupported': 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('int64'), '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': Va
...
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.
Decision Index 0.2.1 runs: Jev-Style v3 (0.8B and 2B)
Self-scored runs of the Decision Index 0.2.1 suite (kit 87d4650, the
rebuilt suite is byte-identical to the lab's: rows b2b56d6f…, added rows 7429f3c9…), made at the request of the board
maintainers (Space discussions #23 and #32).
Every run is complete (scores.json says "complete": true) and was scored with the kit's own scorer.
| Model | Decision Index 0.2.1 | Raw index | Knowledge | Language | Retrieval | Tools | Arts | Scored requests | Median latency | Scores |
|---|---|---|---|---|---|---|---|---|---|---|
Jev-Style-0.8B-Decision-v3 @ 633729d8 |
16.64 | 35.98 | 5.6 | 10.6 | 26.2 | 35.4 | 7.3 | 150,317 (84 unsupported) | 61 ms | scores.json |
Jev-Style-2B-Decision-v3 @ fbb9d676 |
34.83 | 50.14 | 22.3 | 29.0 | 51.6 | 52.8 | 15.9 | 150,317 (0 unsupported) | 136 ms | scores.json |
Area columns are the chance-corrected area skills × 100. Latencies are in-process request wall times on the hardware below (not the maintainers' RTX PRO 6000 measurement).
How the runs were made
- Engine:
engine/jev_engine_torch.py, a kitEngineover each model repo's own PyTorch runtime (jev_style_decision.py) at the pinned revision above, manifest-verified. float32 on CUDA; transformers 5.17.0, torch 2.11.0+cu128, flash-linear-attention 0.5.2 for the Gated DeltaNet layers (checked against the pure PyTorch reference on a 60-request sample: same top answer on 805/805 (2B) and 698/699 (0.8B) questions, max |Δp| ≤ 0.0025). - One fixed rendering and one global temperature for every benchmark; no prompt tuning, no option filtering, nothing
truncated. Requests over a declared limit raise the kit's
Unsupportedand count as unanswered:- 0.8B: 25,600 tokens in total, and question + options + readout within 2,048 tokens; a choice question whose options do not fit is scored in option chunks with one softmax over all options, so only questions whose text alone exceeds the budget are unsupported.
- 2B: 25,600 tokens in total; no separate question/options budget (numbered-option catalogue layout).
- Run with
python -m decision_index run --engine jev_engine_torch:JevStyleTorchEngine --option model=<0.8b|2b> --option revision=<rev> --edition 0.2.1 --compact. Colab reclaimed the VMs several times, so each run was resumed from its ownresults.jsonl, and the last part was split into contiguous row shards run in parallel and merged byrun_id(every scored request appears exactly once; noerrorrows).environment.jsonlists every part. jev-style-0.8b-decision-v3: 92,613 requests on an NVIDIA A100 (Colab), 58,146 on NVIDIA L4 (Colab).jev-style-2b-decision-v3: 86,890 requests on an NVIDIA A100 (Colab), 63,869 on NVIDIA L4 (Colab).- The 2B's
environment.jsonshowstemperature: global (category=None) = None: the engine's provenance line reads a 0.8B-style attribute. The 2B runtime applies its one calibrated global temperature fromreadout_config.json(T = 0.8278650621) to every question; the engine file is published exactly as it was run. results.jsonl.gzis the kit's compact format (no payload or raw output), so the files carry no benchmark text (GPQA and HLE ask that their questions not be redistributed).
Models are Apache-2.0; not affiliated with TypeSafe or Jev.
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