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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
engine: string
engine_options: struct<model: string, mem_gb: double, batch_tokens: int64, model_name: string>
child 0, model: string
child 1, mem_gb: double
child 2, batch_tokens: int64
child 3, model_name: string
model_source: struct<kind: string, repo: string, revision: null, base: string, layers_run: int64, layers_in_checkp (... 172 chars omitted)
child 0, kind: string
child 1, repo: string
child 2, revision: null
child 3, base: string
child 4, layers_run: int64
child 5, layers_in_checkpoint: int64
child 6, head: string
child 7, device: string
child 8, dtype: string
child 9, attn_implementation: string
child 10, context_limit_tokens: int64
child 11, prefix_share: bool
child 12, batch_tokens: int64
child 13, policy: string
torch: string
device: string
cuda: string
gpu: string
transformers: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
child 0, item: string
runner_sha256: string
latency: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
note: string
benchmarks: list<item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: (... 6287 chars omitted)
child 0, item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: int64, err (... 6275 chars omitted)
child 0, catalog_id: int64
child 1, dataset: strin
...
equests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 3, 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 4, 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 5, 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 6, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, GSM8K-10choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 8, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
counts: struct<ok: int64>
child 0, ok: 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'), '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
engine: string
engine_options: struct<model: string, mem_gb: double, batch_tokens: int64, model_name: string>
child 0, model: string
child 1, mem_gb: double
child 2, batch_tokens: int64
child 3, model_name: string
model_source: struct<kind: string, repo: string, revision: null, base: string, layers_run: int64, layers_in_checkp (... 172 chars omitted)
child 0, kind: string
child 1, repo: string
child 2, revision: null
child 3, base: string
child 4, layers_run: int64
child 5, layers_in_checkpoint: int64
child 6, head: string
child 7, device: string
child 8, dtype: string
child 9, attn_implementation: string
child 10, context_limit_tokens: int64
child 11, prefix_share: bool
child 12, batch_tokens: int64
child 13, policy: string
torch: string
device: string
cuda: string
gpu: string
transformers: string
loaded_seconds: double
frozen_corpus_sha256: string
rows_path: list<item: string>
child 0, item: string
runner_sha256: string
latency: string
successful_request_latency_ms: struct<median: double, p95: double, mean: double>
child 0, median: double
child 1, p95: double
child 2, mean: double
note: string
benchmarks: list<item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: (... 6287 chars omitted)
child 0, item: struct<catalog_id: int64, dataset: string, requests: int64, answered: int64, unsupported: int64, err (... 6275 chars omitted)
child 0, catalog_id: int64
child 1, dataset: strin
...
equests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 3, 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 4, 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 5, 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 6, iSarcasmEval-C-En: struct<metric: string, score: null, scored_requests: int64>
child 0, metric: string
child 1, score: null
child 2, scored_requests: int64
child 7, GSM8K-10choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
child 8, GSM8K-4choice: struct<metric: string, score: double, scored_requests: int64>
child 0, metric: string
child 1, score: double
child 2, scored_requests: int64
counts: struct<ok: int64>
child 0, ok: 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'), '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.
Decision Index 0.3 results: AnamK/jdi-ptr14-0.8b
Complete public-suite run of AnamK/jdi-ptr14-0.8b produced with the kit's own runner (python -m decision_index run --edition 0.3, kit commit 9eb2dbe) and engine jdi_ptr14.engine:Ptr14Engine.
| Public index 0.3 | 35.79 (scores.json: complete: true, 140,178 / 140,178) |
| Latency (1× RTX 3090, shared) | median 31.2 ms · mean 52.2 ms · p80 45.5 ms |
| Settings | bf16, sdpa, first 14/24 layers + pointer head ptr_head_L14.pt, shared-prefix batching (batch_tokens=8192, mem_gb=4.5), no truncation |
Files: runs/ptr14nosyn-soup3/{results.jsonl.gz, benchmark-summary.json, index.json, scores.json, environment.json, status.json, SHA256SUMS}.
The only edit after the run: in environment.json the local checkpoint path was replaced by the Hub id AnamK/jdi-ptr14-0.8b (same weights). results.jsonl.gz is the kit's compact form (--compact: payload and raw_output dropped, no suite text); every other field is as written by the runner, and re-scoring it with python -m decision_index score --edition 0.3 reproduces index.json byte for byte.
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